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  • MPI Ethno. Forsch.  (1,171)
  • GBV  (2)
  • Python (Computer program language)
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  • 1
    ISBN: 9781484297452 , 1484297458
    Language: English
    Pages: 1 online resource (xxi, 233 pages) , illustrations
    Edition: [First edition].
    Parallel Title: Erscheint auch als
    Keywords: Machine learning Computer simulation ; Debugging in computer science Computer programs ; Python (Computer program language) ; Apprentissage automatique ; Simulation par ordinateur ; Débogueurs ; Python (Langage de programmation)
    Abstract: This book is for those who wish to understand how Python debugging is and can be used to develop robust and reliable AI, machine learning, and cloud computing software. It will teach you a novel pattern-oriented approach to diagnose and debug abnormal software structure and behavior. The book begins with an introduction to the pattern-oriented software diagnostics and debugging process that, before performing Python debugging, diagnoses problems in various software artifacts such as memory dumps, traces, and logs. Next, you’ll learn to use various debugging patterns through Python case studies that model abnormal software behavior. You’ll also be exposed to Python debugging techniques specific to cloud native and machine learning environments and explore how recent advances in AI/ML can help in Python debugging. Over the course of the book, case studies will show you how to resolve issues around environmental problems, crashes, hangs, resource spikes, leaks, and performance degradation. This includes tracing, logging, and analyziing memory dumps using native WinDbg and GDB debuggers. Upon completing this book, you will have the knowledge and tools needed to employ Python debugging in the development of AI, machine learning, and cloud computing applications.
    Note: Includes bibliographical references and index
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  • 2
    Online Resource
    Online Resource
    [Place of publication not identified] : Packt Publishing
    ISBN: 9781835464694 , 1835464696
    Language: English
    Pages: 1 online resource (1 video file (8 hr., 44 min.)) , sound, color.
    Edition: [First edition].
    DDC: 005.3
    Keywords: Application program interfaces (Computer software) ; Software architecture ; Python (Computer program language) ; Interfaces de programmation d'applications ; Architecture logicielle ; Python (Langage de programmation) ; APIs (interfaces) ; Instructional films ; Nonfiction films ; Internet videos ; Films de formation ; Films autres que de fiction ; Vidéos sur Internet
    Abstract: This course offers a detailed exploration of FastAPI, a cutting-edge Python framework for API development. The course starts with basic API and REST principles, quickly advancing to practical application for a thorough understanding of both theory and practice. The curriculum progresses logically, starting with the basics of FastAPI, including app setup, API routing, linting, and formatting. Participants will create a social media API, manage post and comments, and learn code organization with APIRouter, followed by practical testing experience using pytest. A significant portion of the course focuses on asynchronous databases, vital for scalable application development. It covers database setup, connection, and integration within FastAPI. The course also delves into Python logging techniques, crucial for debugging and monitoring FastAPI applications. It also covers user authentication, an essential element in modern web apps. It includes practical training on JWTs, password hashing, authentication management, and database user relationships, alongside addressing many-to-many database relationships and user email confirmation strategies. In later stages, the course addresses advanced topics like file uploads, background image generation tasks, and deployment methodologies, including continuous integration with GitHub Actions. What you will learn Build and structure a FastAPI application Implement and test RESTful APIs with FastAPI Manage asynchronous database operations Configure and utilize Python logging in FastAPI Secure applications with user authentication and JWTs Handle complex database relationships Deploy FastAPI applications and manage them effectively Audience This course is ideal for aspiring back-end developers, testers looking to integrate automation into their workflows, and front-end developers seeking a deeper understanding of server-side processes. This course will particularly benefit those with a basic grasp of programming and a desire to specialize in the fast-paced realm of web development, API construction, and database management using FastAPI. Prior programming experience in any language is recommended; beginners should consider a basic Python course first, as all required software is provided for free. About the Author Jose Salvatierra Fuentes: Jose, a passionate educator in the realm of coding and software development, has dedicated over 7 years to teaching online. He founded Teclado with a vision to democratize software development education, striving to ensure comprehensive understanding for his students. His approach makes learning coding a clear, rewarding journey, connecting the dots that once seemed obscure. Specializing in Python and JavaScript, Jose excels in web and backend development. He's proficient in various libraries and frameworks, including Flask, React, React Native, and AngularJS. His expertise extends to working with UNIX systems, MongoDB, PostgreSQL, and crafting advanced system architectures. Jose's commitment is to make the learning process both efficient and enjoyable, guiding students to mastery in software development.
    Note: Online resource; title from title details screen (O'Reilly, viewed January 23, 2024)
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  • 3
    Language: English
    Pages: 1 online resource (352 pages) , illustrations
    Edition: Fifth edition.
    Series Statement: Zed Shaw's Hard way series
    DDC: 005.13/3
    Keywords: Python (Computer program language) ; Computer programming ; Python (Langage de programmation) ; Programmation (Informatique) ; computer programming
    Abstract: Zed Shaw has created the world's most reliable system for learning Python. Follow it and you will succeed--just like the millions of beginners Zed has taught to date! You bring the discipline, persistence, and attention; the author supplies the masterful knowledge you need to succeed. In Learn Python the Hard Way, Fifth Edition, you'll learn Python by working through 60 lovingly crafted exercises. Read them. Type in the code. Run it. Fix your mistakes. Repeat. As you do, you'll learn how a computer works, how to solve problems, and how to enjoy programming...even when it's driving you crazy.
    Note: Includes bibliographical references and index
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  • 4
    Online Resource
    Online Resource
    [Place of publication not identified] : Pearson
    ISBN: 9780138298432 , 0138298432
    Language: English
    Pages: 1 online resource (1 video file (2 hr., 22 min.)) , sound, color.
    Edition: [First edition].
    DDC: 005.13/3
    Keywords: Python (Computer program language) ; Computer programming ; Python (Langage de programmation) ; Programmation (Informatique) ; computer programming ; Instructional films ; Nonfiction films ; Internet videos ; Films de formation ; Films autres que de fiction ; Vidéos sur Internet
    Abstract: Sneak Peek The Sneak Peek program provides early access to Pearson video products and is exclusively available to Safari subscribers. Content for titles in this program is made available throughout the development cycle, so products may not be complete, edited, or finalized, including video post-production editing.
    Note: Online resource; title from title details screen (O'Reilly, viewed January 23, 2024)
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  • 5
    ISBN: 9798868800085
    Language: English
    Pages: 1 online resource (538 pages) , illustrations
    Edition: Second edition.
    Parallel Title: Erscheint auch als
    Keywords: Anomaly detection (Computer security) ; Python (Computer program language) ; Machine learning ; Détection d'anomalies (Sécurité informatique) ; Python (Langage de programmation) ; Apprentissage automatique
    Abstract: This beginner-oriented book will help you understand and perform anomaly detection by learning cutting-edge machine learning and deep learning techniques. This updated second edition focuses on supervised, semi-supervised, and unsupervised approaches to anomaly detection. Over the course of the book, you will learn how to use Keras and PyTorch in practical applications. It also introduces new chapters on GANs and transformers to reflect the latest trends in deep learning. Beginning Anomaly Detection Using Python-Based Deep Learning begins with an introduction to anomaly detection, its importance, and its applications. It then covers core data science and machine learning modeling concepts before delving into traditional machine learning algorithms such as OC-SVM and Isolation Forest for anomaly detection using scikit-learn. Following this, the authors explain the essentials of machine learning and deep learning, and how to implement multilayer perceptrons for supervised anomaly detection in both Keras and PyTorch. From here, the focus shifts to the applications of deep learning models for anomaly detection, including various types of autoencoders, recurrent neural networks (via LSTM), temporal convolutional networks, and transformers, with the latter three architectures applied to time-series anomaly detection. This edition has a new chapter on GANs (Generative Adversarial Networks), as well as new material covering transformer architecture in the context of time-series anomaly detection. After completing this book, you will have a thorough understanding of anomaly detection as well as an assortment of methods to approach it in various contexts, including time-series data. Additionally, you will have gained an introduction to scikit-learn, GANs, transformers, Keras, and PyTorch, empowering you to create your own machine learning- or deep learning-based anomaly detectors. What You Will Learn Understand what anomaly detection is, why it it is important, and how it is applied Grasp the core concepts of machine learning. Master traditional machine learning approaches to anomaly detection using scikit-kearn. Understand deep learning in Python using Keras and PyTorch Process data through pandas and evaluate your model's performance using metrics like F1-score, precision, and recall Apply deep learning to supervised, semi-supervised, and unsupervised anomaly detection tasks for tabular datasets and time series applications Who This Book Is For Data scientists and machine learning engineers of all levels of experience interested in learning the basics of deep learning applications in anomaly detection.
    Note: Includes index
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  • 6
    Online Resource
    Online Resource
    San Francisco : No Starch Press
    ISBN: 9781718503250 , 1718503253
    Language: English
    Pages: 1 online resource
    Parallel Title: Erscheint auch als
    DDC: 519.2/3
    Keywords: Algorithms ; Numbers, Random ; Python (Computer program language) ; Algorithmes ; Nombres aléatoires ; Python (Langage de programmation) ; algorithms ; Algorithms ; Numbers, Random ; Python (Computer program language)
    Abstract: "The Art of Randomness teaches readers to harness the power of randomness (and Python code) to solve real-world problems in programming, science, and art through hands-on experiments-from simulating evolution to encrypting messages to making machine-learning algorithms. Each chapter describes how randomness plays into the given topic area, then proceeds to demonstrate its problem-solving role with hands-on experiments to work through using Python code"--
    Note: Includes bibliographical references and index. - Description based on print version record and CIP data provided by publisher; resource not viewed
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  • 7
    Language: English
    Pages: 1 online resource (300 pages) , illustrations
    Edition: First edition.
    DDC: 005.13/3
    Keywords: Python (Computer program language) ; Computer programming ; Python (Langage de programmation) ; Programmation (Informatique) ; computer programming
    Abstract: Keeping up with the Python ecosystem can be daunting. Its developer tooling doesn't provide the out-of-the-box experience native to languages like Rust and Go. When it comes to long-term project maintenance or collaborating with others, every Python project faces the same problem: how to build reliable workflows beyond local development while staying in sync with the evolving ecosystem. With this hands-on guide, Python developers will learn how to forge the moving parts of a Python project into an easy-to-use toolchain, using state-of-the-art tools including Poetry, Nox, GitHub Actions, Dependabot, pytest, mypy, pre-commit, Black, Ruff, and more. Author Claudio Jolowicz shows you how to create robust Python project structures complete with unit tests, static analysis, code formatting, type checking, and documentation as well as continuous integration and delivery.
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  • 8
    Online Resource
    Online Resource
    [Place of publication not identified] : Pearson
    ISBN: 9780138297947 , 0138297940
    Language: English
    Pages: 1 online resource (1 video file (2 hr., 29 min.)) , sound, color.
    Edition: [First edition].
    DDC: 005.13/3
    Keywords: Python (Computer program language) ; Computer programming ; Web site development ; Web applications ; Python (Langage de programmation) ; Programmation (Informatique) ; Sites Web ; Développement ; Applications Web ; computer programming ; Instructional films ; Nonfiction films ; Internet videos ; Films de formation ; Films autres que de fiction ; Vidéos sur Internet
    Abstract: Sneak Peek The Sneak Peek program provides early access to Pearson video products and is exclusively available to Safari subscribers. Content for titles in this program is made available throughout the development cycle, so products may not be complete, edited, or finalized, including video post-production editing. 2.5 Hours of Video Instruction Explore Python's capabilities for Data Science and Machine Learning, the topics of greatest import these days in the tech world. Python has risen to popularity as one of the most versatile and beginner-friendly programming languages. Its simplicity, readability, and extensive libraries make it a powerful language for a wide variety of different domains. It's widely used in web development, data analysis, scientific computing, artificial intelligence, and automation. Python's versatility and large community support make it an excellent language to kickstart your programming journey. This course offers a hands-on approach to building your Python skills through a series of practical projects from scratch. Hone your expertise in areas such as data analysis, machine learning, web scraping, and more. Related Learning: Watch and learn from Shaun's other videos: Functional Programming Projects with Python 3 About the Instructor: Shaun is a Senior Software Developer who specializes in Full-stack development and Software Architecture. He manages teams of developers, as well as teaches many hundreds of thousands more how to create enterprise-ready applications. Shaun's online courses have over 300,000 learners largely because of his passion for development and his focus on helping people apply their programming skills in the real world. He is a life-long programmer and problem-solving addict whose goal is to help people solve meaningful problems by mastering the art of software development. Please don't hesitate to get in touch with him about any opportunities or if you'd like to stay up to date on his other courses and live trainings. Skill Level: Intermediate-Advanced What You Will Learn: Each project will include some different aspect of data science/ machine learning with Python that it will be both accessible and engaging while you advance your skill set. Perform Sentiment Analysis on real-world text Work with Image Recognition Tools Scrape Basic Data from Websites And much more! Who Should Take This Course: Job titles: Software developer, Data analyst/scientist, Machine Learning Engineer, Web Developer, DevOps Engineer. Course Requirements: Prerequisites: Python experience and app development background About Pearson Video Training: Pearson publishes expert-led video tutorials covering a wide selection of technology topics designed to teach you the skills you need to succeed. These professional and personal technology videos feature world-leading author instructors published by your trusted technology brands: Addison-Wesley, Cisco Press, Pearson IT Certification, Prentice Hall, Sams, and Que. Topics include IT Certification, Network Security, Cisco Technology, Programming, Web Development, Mobile Development, and more. Learn more about Pearson Video training at http://www.informit.com/video.
    Note: Online resource; title from title details screen (O'Reilly, viewed January 23, 2024)
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  • 9
    ISBN: 9781098148362 , 1098148363
    Language: English
    Pages: 1 online resource
    Parallel Title: Erscheint auch als
    DDC: 332.64/20285
    Keywords: Electronic trading of securities ; Python (Computer program language) ; Valeurs mobilières ; Commerce électronique ; Python (Langage de programmation)
    Abstract: Deep learning is rapidly gaining momentum in the world of finance and trading. But for many professional traders, this sophisticated field has a reputation for being complex and difficult. This hands-on guide teaches you how to develop a deep learning trading model from scratch using Python, and it also helps you create and backtest trading algorithms based on machine learning and reinforcement learning. Sofien Kaabar--financial author, trading consultant, and institutional market strategist--introduces deep learning strategies that combine technical and quantitative analyses. By fusing deep learning concepts with technical analysis, this unique book presents outside-the-box ideas in the world of financial trading. This A-Z guide also includes a full introduction to technical analysis, evaluating machine learning algorithms, and algorithm optimization. Understand and create machine learning and deep learning models Explore the details behind reinforcement learning and see how it's used in time series Understand how to interpret performance evaluation metrics Examine technical analysis and learn how it works in financial markets Create technical indicators in Python and combine them with ML models for optimization Evaluate the models' profitability and predictability to understand their limitations and potential.
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  • 10
    ISBN: 9781484299913 , 1484299914
    Language: English
    Pages: 1 online resource (xxii, 403 pages) , illustrations
    Edition: 2nd ed.
    Parallel Title: Erscheint auch als
    Keywords: Quantum computing ; Python (Computer program language) ; Informatique quantique ; Python (Langage de programmation)
    Abstract: Learn to write algorithms and program in the new field of quantum computing. This second edition is updated to equip you with the latest knowledge and tools needed to be a complex problem-solver in this ever-evolving landscape. The book has expanded its coverage of current and future advancements and investments by IT companies in this emerging technology. Most chapters are thoroughly revised to incorporate the latest updates to IBM Quantum's systems and offerings, such as improved algorithms, integrating hardware advancements, software enhancements, bug fixes, and more. You'll examine quantum computing in the cloud and run experiments there on a real quantum device. Along the way you'll cover game theory with the Magic Square, an example of quantum pseudo-telepathy. You'll also learn to write code using QISKit, Python SDK, and other APIs such as QASM and execute it against simulators (local or remote) or a real quantum computer. Then peek inside the inner workings of the Bell states for entanglement, Grover's algorithm for linear search, Shor's algorithm for integer factorization, and other algorithms in the fields of optimization, and more. Finally, you'll learn the current quantum algorithms for entanglement, random number generation, linear search, integer factorization, and others. By the end of this book, you'll understand how quantum computing provides massive parallelism and significant computational speedups over classical computers What You'll Learn Write algorithms that provide superior performance over their classical counterparts Create a quantum number generator: the quintessential coin flip with a quantum twist Examine the quantum algorithms in use today for random number generation, linear search, and more Discover quantum teleportation Handle the counterfeit coin problem, a classic puzzle Put your knowledge to the test with more than 150 practice exercises Who This Book Is For Developers, programmers, computer science researchers, teachers, and students.
    Note: Description based upon print version of record
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  • 11
    Online Resource
    Online Resource
    Berkeley, CA : Apress
    ISBN: 9781484299883 , 1484299884
    Language: English
    Pages: 1 online resource (xxiii, 320 pages) , illustrations
    Edition: Second edition.
    Parallel Title: Erscheint auch als
    Keywords: Python (Computer program language) ; Python (Langage de programmation)
    Abstract: This book, which is designed for middle-school through college-aged students, will arm beginners with solid programming foundations they can carry throughout life. It uses fun and simple language (and programming examples) to teach the fundamentals needed to start the down path of becoming a programmer. Python is a highly flexible language, allowing developers to enter any number of technical fields and is a welcome addition to any resume. With its low learning curve, it makes a great introductory language, as new developers can take the coding fundamentals they learn in Python and apply them to any other language. The second edition builds upon the foundation of the first book, revising all the chapters where the language has changed, updating the commands, code, and examples to bring it up to date with the current version of Python. Since Python is the most popular programming language in the world and can be used in conjunction with other languages - across multiple platforms - it can increase the reader's ability to qualify for a wider range of jobs than other languages. Finally, Python is fun - something not every programming language can boast! You will: Install and configure Python Grasp basic software development principles and syntax Understand the best practices for coding in Python Create applications and debug code.
    Note: Includes index. - Online resource; title from PDF title page (SpringerLink, viewed December 15, 2023)
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  • 12
    Online Resource
    Online Resource
    Hoboken, New Jersey : Addison-Wesley Professional
    Language: English
    Pages: 1 online resource (288 pages) , illustrations
    Edition: [First edition].
    DDC: 005.13/3
    Keywords: Python (Computer program language) ; Python (Langage de programmation)
    Abstract: Python is one of the most widely used programming languages in the world. It is used everywhere from primary school education to workaday web development, to the most advanced scientific research institutes of the world. However, like all programming languages, Python has a collection of "Pythonic" ways of accomplishing tasks that are easy to overlook, especially when habits are borrowed wholesale from work in other programming languages. Better Python Code is a guide to Pythonic programming. The book presents common mistakes that Python developers make--even Python developers who have used the language for years--often because Python sometimes presents "attractive nuisances." Throughout, the book is a guide to better programming in the core Python language. Each section shows a concrete but concise example of some misunderstanding or bad habit in action. Each section contains a description of what is wrong with the sample code and a suggestion for one or more better ways to code equivalent functionality without the initial pitfall. Every pitfall addressed in this book reflects foibles, errors, and misunderstandings that the author as seen in concrete, widely used code bases written by experienced developers, over his 25 years of writing Python. Both beginners and developers with decades of experience will learn to correct limitations in the code they write after reflecting on these discussions.
    Note: Includes bibliographical references and index
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  • 13
    Online Resource
    Online Resource
    Sebastopol : O'Reilly Media, Incorporated
    ISBN: 9781098145316 , 1098145313
    Language: English
    Pages: 1 online resource (352 p.)
    Edition: 3rd ed.
    Parallel Title: Erscheint auch als
    DDC: 006.3/12
    Keywords: Data mining ; Python (Computer program language)
    Abstract: If programming is magic, then web scraping is surely a form of wizardry. By writing a simple automated program, you can query web servers, request data, and parse it to extract the information you need. This thoroughly updated third edition not only introduces you to web scraping but also serves as a comprehensive guide to scraping almost every type of data from the modern web. Part I focuses on web scraping mechanics: using Python to request information from a web server, performing basic handling of the server's response, and interacting with sites in an automated fashion. Part II explores a variety of more specific tools and applications to fit any web scraping scenario you're likely to encounter. Parse complicated HTML pages Develop crawlers with the Scrapy framework Learn methods to store the data you scrape Read and extract data from documents Clean and normalize badly formatted data Read and write natural languages Crawl through forms and logins Scrape JavaScript and crawl through APIs Use and write image-to-text software Avoid scraping traps and bot blockers Use scrapers to test your website.
    Note: Description based upon print version of record. - Markov Models
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  • 14
    Language: English
    Pages: 1 online resource (400 pages) , illustrations
    Edition: First edition.
    DDC: 005.7
    Keywords: Big data ; Data mining ; Python (Computer program language) ; Données volumineuses ; Exploration de données (Informatique) ; Python (Langage de programmation)
    Abstract: Want to speed up your data analysis and work with larger-than-memory datasets? Python Polars offers a blazingly fast, multithreaded, and elegant API for data loading, manipulation, and processing. With this hands-on guide, you'll walk through every aspect of Polars and learn how to tackle practical use cases using real-world datasets. Jeroen Janssens and Thijs Nieuwdorp from Xomnia in Amsterdam show you how this superfast DataFrame library is perfect for efficient data wrangling, ETL pipelines, and so much more. This book helps you quickly learn the syntax and understand Polars' underlying concepts. You don't need to have experience with pandas or Spark, but if you do, this book will help you make a smooth transition.
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  • 15
    Online Resource
    Online Resource
    [Place of publication not identified] : Packt Publishing
    ISBN: 9781835463444 , 1835463444
    Language: English
    Pages: 1 online resource (1 video file (10 hr., 14 min.)) , sound, color.
    Edition: [First edition].
    DDC: 006.7/6
    Keywords: Web site development Computer programs ; Application software Development ; Python (Computer program language) ; Sites Web ; Systèmes-auteur ; Logiciels d'application ; Développement ; Python (Langage de programmation) ; Instructional films ; Nonfiction films ; Internet videos ; Films de formation ; Films autres que de fiction ; Vidéos sur Internet
    Abstract: Next.js is a popular open-source JavaScript framework designed to make web development with React efficient and scalable. Next.js adds layers of abstraction to React and simplifies the process of building modern web applications. It provides a set of conventions and features that enhance the development experience, making it easier to create performant and SEO-friendly applications. This comprehensive Next.js course unfolds by beginning with an introduction to web development fundamentals, as it progresses through React basics, advanced Next.js concepts, and real-world deployment strategies. We will explore key topics such as server-side rendering (SSR), client-side rendering (CSR), static site generation (SSG), and incremental static regeneration (ISR). The course offers hands-on experience with a final project, covering everything from structuring applications to optimizing for performance and SEO. The modular format accommodates varying skill levels, ensuring a well-rounded and practical learning experience for all learners. Upon completion of this Next.js course, you will become proficient in web development fundamentals, React essentials, and advanced Next.js concepts. Through hands-on experience, master server-side rendering, client-side rendering, dynamic routing, and performance and scalability in real-world scenarios. What you will learn Master SSR, CSR, and SSG for optimized web development Explore React basics and advanced component development in Next.js Implement Next.js best practices for efficient and scalable web applications Learn to create dynamic routes for versatile content handling Deploy fully functional applications using Vercel and Git/GitHub workflows Enhance applications for search engines and social platforms through metadata and SEO practices Audience This Next.js course is tailored for a diverse audience, catering to both entry-level and seasoned developers. Ideal for junior developers, it provides a strong foundation in web development fundamentals, React basics, and advanced Next.js concepts. Intermediate developers can enhance their skills and gain mastery in the latest features of Next.js. Senior web developers seeking to stay updated with modern concepts will find valuable insights for optimizing web applications. No programming experience is required, but basic web development and JavaScript understanding is desirable for the course. About the Author Clarian North: Clarian North is a recognized industry-certified instructor, CEO of Orbital LLC, and a senior project engineer with over 90K enrolments worldwide. His students have gone on to work for some of the biggest production companies such as Universal, Google, Amazon, Warner, and the BBC. He has contributed to viral streams, grown a unique digital imprint in over 80 countries, and been featured in Rolling Stone, Billboard, Guardian, Bloomberg, and Vice. He has worked for majors, indies, and international publishing houses, including Grammy-nominated and award-winning productions, mixing for Emirates Airline Inflight Radio, and producing for Kompakt Records. He is dedicated to giving accessible and comprehensive tech courses, teaching complex subject matter with clear and example-based explanations, and guiding students through complex techniques with just a computer and free software.
    Note: Online resource; title from title details screen (O'Reilly, viewed January 31, 2024)
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  • 16
    ISBN: 9781484298619 , 1484298616
    Language: English
    Pages: 1 online resource (578 p.)
    Edition: 2nd ed.
    Parallel Title: Erscheint auch als
    Keywords: Python (Computer program language) ; Microcontrollers ; Computer programming ; Python (Langage de programmation) ; Microcontrôleurs ; Programmation (Informatique) ; computer programming
    Abstract: This book will help you quickly learn to program for microcontrollers and IoT devices without a lot of study and expense. MicroPython and controllers that support it eliminate the need for programming in a C-like language, making the creation of IoT applications and devices easier and more accessible than ever. MicroPython for the Internet of Things is ideal for readers new to electronics and the world of IoT. Specific examples are provided covering a range of supported devices, sensors, and MicroPython boards such as the Raspberry Pi Pico and the Arduino Nano Connect RP2040 board. Programming for microcontrollers has never been easier. The book takes a practical and hands-on approach without a lot of detours into the depths of theory. It'll show you a faster and easier way to program microcontrollers and IoT devices, teach you MicroPython, a variant of one of the most widely used scripting languages, and is written to be accessible to those new to electronics. After completing this book, and its fun example projects, you'll be ready to ready to use MicroPython to develop your own IoT applications. What You Will Learn Program in MicroPython Understand sensors and basic electronics Develop your own IoT projects Build applications for popular boards such as Raspberry Pi Pico and Arduino Nano Connect RP2040 Load MicroPython on compatible boards Interface with hardware breakout boards Connect hardware to software through MicroPython Explore connecting your microcontroller to the cloud Develop IoT projects for the cloud Who This Book Is For Anyone interested in building IoT solutions without the heavy burden of programming in C++ or C. The book also appeals to those wanting an easier way to work with hardware than is provided by platforms that require more complex programming environments.
    Note: Description based upon print version of record. - Conditional Statements
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  • 17
    Online Resource
    Online Resource
    [Place of publication not identified] : Packt Publishing
    ISBN: 9781835885628 , 1835885624
    Language: English
    Pages: 1 online resource (1 video file (11 hr., 11 min.)) , sound, color.
    Edition: [First edition].
    DDC: 006.3
    Keywords: Artificial intelligence ; Python (Computer program language) ; Intelligence artificielle ; Python (Langage de programmation) ; artificial intelligence ; Instructional films ; Nonfiction films ; Internet videos ; Films de formation ; Films autres que de fiction ; Vidéos sur Internet
    Abstract: Embark on a transformative learning experience with 'Master ChatGPT and OpenAI APIs By Building AI Tools in Python.' This immersive course takes you from the basics of ChatGPT and OpenAI's potent APIs through a journey of building dynamic AI-powered tools. Discover the magic behind ChatGPT's conversational prowess and dive deep into the neural networks and transformers that make it tick. You'll start by creating and managing your ChatGPT account, understanding the model's anatomy, and grasping the limitations and capabilities of AI language models. Transitioning from theory to hands-on practice, the course guides you through content creation, code generation, and the intricacies of prompt engineering. Moreover, you'll venture into the realms of generative AI with Midjourney, craft AI-driven applications, and harness the synergy of OpenAI and DALL-E for on-the-fly image generation. This course demystifies the AI concepts crucial in today's technology landscape, providing you with the skills to innovate and excel in the burgeoning field of AI. What you will learn Navigate the foundational elements of ChatGPT and OpenAI APIs Generate creative content and code with ChatGPT's language model Design and implement AI applications with user-friendly interfaces Master the art of prompt engineering for optimized AI interactions Build AI-powered tools and images using OpenAI and DALL-E Develop secure, intelligent applications with advanced Python techniques Audience This course welcomes everyone from entrepreneurs to artists and developers, aiming to learn about cutting-edge AI technologies and build AI-powered applications. Basic programming knowledge is preferred but not mandatory, making this course accessible to a wide range of learners. About the Author Paulo Dichone: Paulo Dichone, a seasoned software engineer and AWS Cloud Practitioner, is renowned for his expertise in Android, Flutter, and AWS, as well as being a best-selling instructor. Paulo has successfully imparted his knowledge to over 200,000 students across 175 countries, specializing in mobile app development for Android and iOS, web development, and AWS Cloud. His teaching philosophy centers on empowering students to excel as developers and AWS cloud practitioners, regardless of their prior experience. Beyond his professional pursuits, Paulo is devoted to his family, enjoys playing the guitar and mandolin, and loves to travel. He is committed to guiding students to achieve their highest potential in the tech industry.
    Note: Online resource; title from title details screen (O'Reilly, viewed March 4, 2024)
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  • 18
    ISBN: 9781835081495 , 1835081495 , 9781835081167
    Language: English
    Pages: 1 online resource (220 p.)
    DDC: 005.13/3
    Keywords: Python (Computer program language) ; Computer software Development ; Python (Langage de programmation)
    Abstract: Unleash DevOps excellence with Python and its ecosystem of tools for seamless orchestration on both local and cloud platforms, such as GCP, AWS, and AzureKey FeaturesIntegrate Python into DevOps for streamlined workflows, task automation, and improved collaborationCombine the principles of Python and DevOps into a unified approach for problem solving.
    Note: Description based upon print version of record. - Sample 1: Running fleet maintenance on multiple instance fleets at once
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  • 19
    ISBN: 9798868802218
    Language: English
    Pages: 1 online resource (220 p.)
    Parallel Title: Erscheint auch als
    Keywords: Application software Development ; Information visualization ; Python (Computer program language) ; Dashboards (Management information systems) ; Logiciels d'application ; Développement ; Visualisation de l'information ; Python (Langage de programmation) ; Tableaux de bord (Gestion)
    Abstract: Create interactive and data-driven dashboards using Python. This hands-on guide is a practical resource for those (with modest programming skills) in scientific and engineering fields looking to leverage Python's power for data visualization and analysis in a user-friendly dashboard format. You'll begin by gaining a fundamental understanding of Python programming, including data types, lists, dictionaries, and data structures. The book then delves into the world of reactive programming with Plotly and Dash, offering a hands-on approach to building interactive web-based dashboards. Next, you'll see how to work with online data, how to scrape and clean datasets, and keep files up-to-date. The book also guides you through planning a dashboard prototype, outlining project tasks, trends, forecasts, spectra, and other design considerations. It concludes with a discussion of how the dashboard can be used for data visualization of real data, explaining the usefulness of tools such as spectra. By providing detailed examples for download and customization, Prototyping Python Dashboards for Scientists and Engineers will equip you with the skills needed to jumpstart your own development efforts. What You'll Learn Design a dashboard with Python Convert and filter Excel formatted files to produce CSV files Create browser-served graphics with PLOTLY Generate polynomial trend lines for forecasting Build a Unix service to share your dashboard Who This Book Is For Scientists, engineers, students, programmers, and data enthusiasts who aspire to harness Python's potential for data visualization and analysis through the creation of interactive dashboards. Many will be pragmatic programmers with modest skills and limited resources who mainly want to see a working solution they could emulate.
    Note: Description based upon print version of record
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  • 20
    Online Resource
    Online Resource
    [Place of publication not identified] : Packt Publishing
    ISBN: 9781835885925 , 1835885926
    Language: English
    Pages: 1 online resource (1 video file (8 hr., 44 min.)) , sound, color.
    Edition: [First edition].
    DDC: 004.6
    Keywords: Artificial intelligence ; Computer programming ; Chatbots ; Application program interfaces (Computer software) ; Python (Computer program language) ; Intelligence artificielle ; Programmation (Informatique) ; Interfaces de programmation d'applications ; Python (Langage de programmation) ; artificial intelligence ; computer programming ; APIs (interfaces) ; Instructional films ; Nonfiction films ; Internet videos ; Films de formation ; Films autres que de fiction ; Vidéos sur Internet
    Abstract: This comprehensive masterclass takes you on a transformative journey into the realm of LangChain and Large Language Models, equipping you with the skills to build autonomous AI tools. Starting with the basics, you'll set up your development environment, including OpenAI API and Python, and progress to advanced topics like LangChain's architecture, prompt templates, and parsers. The course meticulously guides you through creating complex chains, memory models, and agents, culminating in the development of practical applications such as PDF extractors, newsletter generators, and multi-document chatbots. With hands-on tutorials, you'll learn to leverage LangChain for tasks ranging from document loading and splitting to embedding vector stores for semantic similarity searches. By the end, you'll have the knowledge to implement AI in creative and impactful ways, from image-to-text conversion to building interactive chatbots and more, all while navigating the ethical considerations of AI deployment. What you will learn Configure OpenAI API and Python for AI development Create and manipulate LangChain prompt templates and parsers Implement LangChain memory models and chains for complex AI applications Develop real-world applications, including newsletter generators and chatbots Work with LangChain embeddings and vector stores for semantic searches Navigate the ethical and copyright implications of AI-generated content Audience This course is designed for a broad audience interested in artificial intelligence, from data scientists enhancing projects with AI and LangChain, to product managers boosting user experience with AI features. AI enthusiasts, tech innovators, and programmers will deepen their understanding of LangChain, unlocking new opportunities in AI-driven development and pioneering next-gen solutions. While specific knowledge of Python is not necessary, familiarity with programming concepts is essential. About the Author Paulo Dichone: Paulo Dichone, a seasoned software engineer and AWS Cloud Practitioner, is renowned for his expertise in Android, Flutter, and AWS, as well as being a best-selling instructor. Paulo has successfully imparted his knowledge to over 200,000 students across 175 countries, specializing in mobile app development for Android and iOS, web development, and AWS Cloud. His teaching philosophy centers on empowering students to excel as developers and AWS cloud practitioners, regardless of their prior experience. Beyond his professional pursuits, Paulo is devoted to his family, enjoys playing the guitar and mandolin, and loves to travel. He is committed to guiding students to achieve their highest potential in the tech industry.
    Note: Online resource; title from title details screen (O'Reilly, viewed March 4, 2024)
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  • 21
    ISBN: 9781804612415 , 1804612413 , 9781804618127
    Language: English
    Pages: 1 online resource
    Edition: 1st edition.
    Parallel Title: Erscheint auch als
    DDC: 005.13/3
    Keywords: Machine learning ; Python (Computer program language) ; Data mining ; Apprentissage automatique ; Python (Langage de programmation) ; Exploration de données (Informatique)
    Abstract: Join the data-centric revolution and master the concepts, techniques, and algorithms shaping the future of AI and ML development, using Python Key Features Grasp the principles of data centricity and apply them to real-world scenarios Gain experience with quality data collection, labeling, and synthetic data creation using Python Develop essential skills for building reliable, responsible, and ethical machine learning solutions Purchase of the print or Kindle book includes a free PDF eBook Book Description In the rapidly advancing data-driven world where data quality is pivotal to the success of machine learning and artificial intelligence projects, this critically timed guide provides a rare, end-to-end overview of data-centric machine learning (DCML), along with hands-on applications of technical and non-technical approaches to generating deeper and more accurate datasets. This book will help you understand what data-centric ML/AI is and how it can help you to realize the potential of 'small data'. Delving into the building blocks of data-centric ML/AI, you'll explore the human aspects of data labeling, tackle ambiguity in labeling, and understand the role of synthetic data. From strategies to improve data collection to techniques for refining and augmenting datasets, you'll learn everything you need to elevate your data-centric practices. Through applied examples and insights for overcoming challenges, you'll get a roadmap for implementing data-centric ML/AI in diverse applications in Python. By the end of this book, you'll have developed a profound understanding of data-centric ML/AI and the proficiency to seamlessly integrate common data-centric approaches in the model development lifecycle to unlock the full potential of your machine learning projects by prioritizing data quality and reliability. What you will learn Understand the impact of input data quality compared to model selection and tuning Recognize the crucial role of subject-matter experts in effective model development Implement data cleaning, labeling, and augmentation best practices Explore common synthetic data generation techniques and their applications Apply synthetic data generation techniques using common Python packages Detect and mitigate bias in a dataset using best-practice techniques Understand the importance of reliability, responsibility, and ethical considerations in ML/AI Who this book is for This book is for data science professionals and machine learning enthusiasts looking to understand the concept of data-centricity, its benefits over a model-centric approach, and the practical application of a best-practice data-centric approach in their work. This book is also for other data professionals and senior leaders who want to explore the tools and techniques to improve data quality and create opportunities for small data ML/AI in their organizations.
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  • 22
    Online Resource
    Online Resource
    [Place of publication not identified] : Manning Publications
    Language: English
    Pages: 1 online resource (1 sound file (5 hr., 7 min.))
    Edition: [First edition].
    DDC: 005.13/3
    Keywords: Python (Computer program language) Juvenile literature ; Computer programming Juvenile literature ; Python (Computer program language) ; Computer programming ; Python (Langage de programmation) ; Ouvrages pour la jeunesse ; Programmation (Informatique) ; Ouvrages pour la jeunesse ; Audiobooks ; Livres audio
    Abstract: Time to take an adventure with friends! Team up with Erik and Simon to learn Python the easy way. This colorful book uses engaging questions and lively conversations to introduce computer programming to young readers one step at a time. In A Pythonic Adventure, you will learn useful Python skills like: Installing Python Working with files Creating text-based dialogs and menus Using if/then, loops, lists, dictionaries, and input/output Building web applications Making your web apps look super professional It's fun to learn with friends! In A Pythonic Adventure you'll meet Erik and Simon, two brothers who are just beginning their Python journey. Join them as they chat about the language, learn the basics, and build some cool programs. The book's dialogue helps young programmers understand complex concepts much more easily. It's the perfect way for young programmers (and their parents) to get started. There's no boring lessons or dull exercises in this adventure. You'll follow Erik and Simon's questions and mistakes, discover how to write programs with a team, and get a chance to create applications you can use in your daily life. By the time they're done reading, young learners will not only know how to write code, they'll know how to think about problems like professional developers. All code in this book runs on Mac, Windows, Linux, and Raspberry Pi. About the Technology Computer programming is an adventure, full of new experiences, challenges, triumphs, and mistakes. In A Pythonic Adventure, you'll join brothers Erik and Simon as they learn to create their first Python program. Written especially for young readers, this book is the perfect introduction to a skill that will last a lifetime! About the Book A Pythonic Adventure teaches you to code by asking questions, making errors, and trying out different solutions--just like in real life. As you go, you'll create a web application for a coffee shop step-by-step, from your first online menu to saving orders in a database. And this unique tutorial goes deeper than other beginner books. You'll learn and practice important skills like planning applications, finding bugs, and managing user expectations. What's Inside Installing Python Creating text-based dialogs and menus Building web applications Making your web apps look professional About the Reader For readers aged 10+. Perfect for adult beginners, too! About the Author Pavel Anni is a Principal Customer Engineer at SambaNova Systems, and has also worked for Sun Microsystems, Oracle, and Red Hat. Quotes Pavel's conversational writing style is engaging and entertaining. If Plato wrote a programming book, he'd have written it like this one! - Nicholas Tollervey, Anaconda A great way to build your first application. Th e final web app is impressive! - Andrew R. Freed, IBM This book was perfect for sparking my son into coding with Python--now I can't stop him! - Ben McNamara, DataGeek This book awakened my daughters' interest in programming. They will be part of the future. - Walter Alexander Mata L©đpez, University of Colima.
    Note: Online resource; title from title details screen (O'Reilly, viewed March 4, 2024)
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  • 23
    Online Resource
    Online Resource
    [Place of publication not identified] : Scatterplot Press
    ISBN: 9781835461969
    Language: English
    Pages: 1 online resource (146 pages) , illustrations
    Edition: First edition.
    DDC: 005.13/3
    Keywords: Machine learning ; Python (Computer program language) ; Data mining ; Apprentissage automatique ; Python (Langage de programmation) ; Exploration de données (Informatique)
    Abstract: The course starts by setting the foundation with an introduction to machine learning, Python, and essential libraries, ensuring you grasp the basics before diving deeper. It then progresses through exploratory data analysis, data scrubbing, and pre-model algorithms, equipping you with the skills to understand and prepare your data for modeling. The journey continues with detailed walkthroughs on creating, evaluating, and optimizing machine learning models, covering key algorithms such as linear and logistic regression, support vector machines, k-nearest neighbors, and tree-based methods. Each section is designed to build upon the previous, reinforcing learning and application of concepts. Wrapping up, the course introduces the next steps, including an introduction to Python for newcomers, ensuring a comprehensive understanding of machine learning applications.
    Note: Includes bibliographical references
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  • 24
    ISBN: 9781805125419 , 1805125419 , 9781805127161
    Language: English
    Pages: 1 online resource.
    Edition: Third Edition.
    Series Statement: Expert insight
    Parallel Title: Erscheint auch als
    DDC: 005.13/3
    Keywords: Python (Computer program language) ; Natural language processing (Computer science) ; Bayesian statistical decision theory ; Python (Langage de programmation) ; Traitement automatique des langues naturelles ; Théorie de la décision bayésienne
    Abstract: Learn the fundamentals of Bayesian modeling using state-of-the-art Python libraries, such as PyMC, ArviZ, Bambi, and more, guided by an experienced Bayesian modeler who contributes to these libraries Key Features Conduct Bayesian data analysis with step-by-step guidance Gain insight into a modern, practical, and computational approach to Bayesian statistical modeling Enhance your learning with best practices through sample problems and practice exercises Purchase of the print or Kindle book includes a free PDF eBook. Book DescriptionThe third edition of Bayesian Analysis with Python serves as an introduction to the main concepts of applied Bayesian modeling using PyMC, a state-of-the-art probabilistic programming library, and other libraries that support and facilitate modeling like ArviZ, for exploratory analysis of Bayesian models; Bambi, for flexible and easy hierarchical linear modeling; PreliZ, for prior elicitation; PyMC-BART, for flexible non-parametric regression; and Kulprit, for variable selection. In this updated edition, a brief and conceptual introduction to probability theory enhances your learning journey by introducing new topics like Bayesian additive regression trees (BART), featuring updated examples. Refined explanations, informed by feedback and experience from previous editions, underscore the book's emphasis on Bayesian statistics. You will explore various models, including hierarchical models, generalized linear models for regression and classification, mixture models, Gaussian processes, and BART, using synthetic and real datasets. By the end of this book, you will possess a functional understanding of probabilistic modeling, enabling you to design and implement Bayesian models for your data science challenges. You'll be well-prepared to delve into more advanced material or specialized statistical modeling if the need arises. What you will learn Build probabilistic models using PyMC and Bambi Analyze and interpret probabilistic models with ArviZ Acquire the skills to sanity-check models and modify them if necessary Build better models with prior and posterior predictive checks Learn the advantages and caveats of hierarchical models Compare models and choose between alternative ones Interpret results and apply your knowledge to real-world problems Explore common models from a unified probabilistic perspective Apply the Bayesian framework's flexibility for probabilistic thinking Who this book is for If you are a student, data scientist, researcher, or developer looking to get started with Bayesian data analysis and probabilistic programming, this book is for you. The book is introductory, so no previous statistical knowledge is required, although some experience in using Python and scientific libraries like NumPy is expected.
    Note: Includes bibliographical references and index. - Online resource; title from PDF title page (EBSCO, viewed February 29, 2024)
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  • 25
    Online Resource
    Online Resource
    [Sebastopol, California] : O'Reilly Media, Inc.
    ISBN: 9781098171551 , 1098171551
    Language: English
    Pages: 1 online resource (5 pages)
    Edition: [First edition].
    Series Statement: Shortcuts
    DDC: 005.13/3
    Keywords: Python (Computer program language) ; Python (Langage de programmation)
    Abstract: Build your knowledge of Python, the world's favorite programming language, with this Shortcuts collection. These recipes address a range of well-known and more advanced function definitions, as well as common programming patterns for class and object definitions.
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  • 26
    Online Resource
    Online Resource
    [Sebastopol, California] : O'Reilly Media, Inc.
    ISBN: 9781098171360 , 1098171365
    Language: English
    Pages: 1 online resource (5 pages)
    Edition: [First edition].
    Series Statement: Shortcuts
    DDC: 005.13/3
    Keywords: Python (Computer program language) ; Python (Langage de programmation)
    Abstract: Build your knowledge of Python with this Shortcuts collection. Focusing on common problems involving text manipulation, many of these tasks can be solved using built-in methods of strings. More complicated operations may require regular expressions or the creation of a full-fledged parser.
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  • 27
    ISBN: 9781837639533
    Language: English
    Pages: 1 online resource (814 pages) , illustrations
    Edition: Second edition.
    Series Statement: Expert insight
    DDC: 001.4/226028566
    Keywords: Information visualization Computer programs ; Visual analytics Data processing ; Data mining Computer programs ; Business intelligence Computer programs ; R (Computer program language) ; Python (Computer program language) ; Visualisation de l'information ; Logiciels ; Analyse visuelle ; Informatique ; Exploration de données (Informatique) ; Logiciels ; R (Langage de programmation) ; Python (Langage de programmation)
    Abstract: The latest edition of this book delves deep into advanced analytics, focusing on enhancing Python and R proficiency within Power BI. New chapters cover optimizing Python and R settings, utilizing Intel's Math Kernel Library (MKL) for performance boosts, and addressing integration challenges. Techniques for managing large datasets beyond available RAM, employing the Parquet data format, and advanced fuzzy matching algorithms are explored. Additionally, it discusses leveraging SQL Server Language Extensions to overcome traditional Python and R limitations in Power BI. It also helps in crafting sophisticated visualizations using the Grammar of Graphics in both R and Python. This Power BI book will help you master data validation with regular expressions, import data from diverse sources, and apply advanced algorithms for transformation. You'll learn how to safeguard personal data in Power BI with techniques like pseudonymization, anonymization, and data masking. You'll also get to grips with the key statistical features of datasets by plotting multiple visual graphs in the process of building a machine learning model. The book will guide you on utilizing external APIs for enrichment, enhancing I/O performance, and leveraging Python and R for analysis. You'll reinforce your learning with questions at the end of each chapter.
    Note: Includes bibliographical references and index
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  • 28
    Online Resource
    Online Resource
    Tōkyō-to Shinjuku-ku : Orairī Japan
    Orig.schr. Ausgabe: 初版.
    Title: Raspberry Piクックブック : : 第 4版 /
    Publisher: 東京都新宿区 : オライリー・ジャパン
    ISBN: 9784814400508 , 4814400500
    Language: Japanese
    Pages: 1 online resource (560 pages) , illustrations.
    Edition: Shohan.
    Series Statement: Make: projects
    Uniform Title: Raspberry Pi cookbook
    DDC: 004.1675
    Keywords: Raspberry Pi (Computer) ; Python (Computer program language) ; Application software Development ; Raspberry Pi (Ordinateur) ; Python (Langage de programmation) ; Logiciels d'application ; Développement
    Abstract: "If you've started to work with Raspberry Pi, you know that Raspberry Pi's capabilities are continually expanding. The fourth edition of this popular cookbook provides more than 200 hands-on recipes (complete with code) that show you how to run this tiny low-cost computer with Linux, program it with Python, hook it up to sensors and motors, and use it with the Internet of things (IoT). This new edition includes new chapters on the Raspberry Pi Pico and machine learning with the Raspberry Pi."--
    Note: In Japanese.
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  • 29
    Online Resource
    Online Resource
    [Sebastopol, California] : O'Reilly Media, Inc.
    ISBN: 9781098171339 , 1098171330
    Language: English
    Pages: 1 online resource (5 pages)
    Edition: [First edition].
    Series Statement: Shortcuts
    DDC: 005.13/3
    Keywords: Python (Computer program language) ; Python (Langage de programmation)
    Abstract: Build your knowledge of Python with this Shortcuts collection. Focusing on common problems involving text manipulation, many of these tasks can be solved using built-in methods of strings. More complicated operations may require regular expressions or the creation of a full-fledged parser.
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  • 30
    Online Resource
    Online Resource
    [Sebastopol, California] : O'Reilly Media, Inc.
    ISBN: 9781098171315 , 1098171314
    Language: English
    Pages: 1 online resource (5 pages)
    Edition: [First edition].
    Series Statement: Shortcuts
    DDC: 005.13/3
    Keywords: Python (Computer program language) ; Python (Langage de programmation)
    Abstract: Build your knowledge of Python with this Shortcuts collection. Focusing on common problems involving text manipulation, many of these tasks can be solved using built-in methods of strings. More complicated operations may require regular expressions or the creation of a full-fledged parser.
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  • 31
    ISBN: 9781805122739 , 1805122738 , 9781805129233
    Language: English
    Pages: 1 online resource
    Edition: 1st edition.
    Parallel Title: Erscheint auch als
    DDC: 519.5/5
    Keywords: Time-series analysis Data processing ; Deep learning (Machine learning) ; Python (Computer program language) ; Série chronologique ; Informatique ; Apprentissage profond ; Python (Langage de programmation)
    Abstract: Learn how to deal with time series data and how to model it using deep learning and take your skills to the next level by mastering PyTorch using different Python recipes Key Features Learn the fundamentals of time series analysis and how to model time series data using deep learning Explore the world of deep learning with PyTorch and build advanced deep neural networks Gain expertise in tackling time series problems, from forecasting future trends to classifying patterns and anomaly detection Purchase of the print or Kindle book includes a free PDF eBook Book Description Most organizations exhibit a time-dependent structure in their processes, including fields such as finance. By leveraging time series analysis and forecasting, these organizations can make informed decisions and optimize their performance. Accurate forecasts help reduce uncertainty and enable better planning of operations. Unlike traditional approaches to forecasting, deep learning can process large amounts of data and help derive complex patterns. Despite its increasing relevance, getting the most out of deep learning requires significant technical expertise. This book guides you through applying deep learning to time series data with the help of easy-to-follow code recipes. You'll cover time series problems, such as forecasting, anomaly detection, and classification. This deep learning book will also show you how to solve these problems using different deep neural network architectures, including convolutional neural networks (CNNs) or transformers. As you progress, you'll use PyTorch, a popular deep learning framework based on Python to build production-ready prediction solutions. By the end of this book, you'll have learned how to solve different time series tasks with deep learning using the PyTorch ecosystem. What you will learn Grasp the core of time series analysis and unleash its power using Python Understand PyTorch and how to use it to build deep learning models Discover how to transform a time series for training transformers Understand how to deal with various time series characteristics Tackle forecasting problems, involving univariate or multivariate data Master time series classification with residual and convolutional neural networks Get up to speed with solving time series anomaly detection problems using autoencoders and generative adversarial networks (GANs) Who this book is for If you're a machine learning enthusiast or someone who wants to learn more about building forecasting applications using deep learning, this book is for you. Basic knowledge of Python programming and machine learning is required to get the most out of this book.
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  • 32
    Online Resource
    Online Resource
    [Place of publication not identified] : Packt Publishing
    ISBN: 9781836204473 , 1836204477
    Language: English
    Pages: 1 online resource (1 video file (4 hr., 34 min.)) , sound, color.
    Edition: [First edition].
    DDC: 005.13/3
    Keywords: Python (Computer program language) ; Computer programming ; Python (Langage de programmation) ; Programmation (Informatique) ; computer programming ; Instructional films ; Nonfiction films ; Internet videos ; Films de formation ; Films autres que de fiction ; Vidéos sur Internet
    Abstract: An immersive video course designed to introduce and deepen your understanding of Object-Oriented Programming (OOP) principles within the Python ecosystem. Through a hands-on approach, instructors Vonne and Justin guide you from the foundational setup and OOP basics--encapsulation, abstraction, inheritance, and polymorphism--to the creation of a dynamic text-based game, MonsterSlash. This course is structured to enhance your programming skills by applying OOP techniques to reduce code redundancy, promote reusability, and streamline your coding workflow. You'll embark on a project-driven learning path that not only solidifies theoretical concepts but also puts them into practice by developing a fully functional game. As the course progresses, you'll refine your game, add complexity, and implement advanced OOP strategies, culminating in version 2 of MonsterSlash that features enhanced gameplay elements. Whether you're a beginner eager to explore programming or an intermediate looking to sharpen your OOP skills in Python, this course offers valuable insights and skills for your development arsenal. What you will learn Set up your Python environment for OOP development. Understand and apply the four pillars of OOP in Python. Create, extend, and refine classes and objects. Develop a text-based game from scratch using OOP principles. Employ inheritance and composition to optimize your code. Refactor and enhance your game with advanced OOP techniques. Audience This course is ideal for individuals interested in Python programming, especially those curious about object-oriented programming. Beginners will find the course accessible, while intermediate programmers can deepen their OOP knowledge. Prior exposure to basic Python syntax is helpful but not required. About the Authors ACI Learning: ACI Learning trains leaders in Cybersecurity, Audit, and Information Technology. Whether starting an IT career, mastering a profession, or developing a team, they provide essential support at every step. Justin Dennison: Justin leads AWS and developer content creation for ITProTV. He has experience in multiple programming languages. His certifications include AWS Certified Solutions Architect -- Associate, AWS Certified Developer -- Associate, AWS Certified Cloud Practitioner, Oracle Certified Associate, Java SE 7 Programmer. Vonne Smith: Vonne leads the content development for the OfficeProTV and CreativeProTV channels. She has over a decade of experience teaching Office and Adobe applications both online and in the classroom. Certifications: Microsoft Office Master 2016.
    Note: Online resource; title from title details screen (O'Reilly, viewed April 23, 2024)
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  • 33
    Online Resource
    Online Resource
    [Place of publication not identified] : O'Reilly Media, Inc.
    Language: English
    Pages: 1 online resource (1 video file (2 hr., 56 min.)) , sound, color.
    Edition: [First edition].
    DDC: 005.7
    Keywords: Electronic data processing ; Software engineering ; Big data ; Python (Computer program language) ; Computer programming ; Cloud computing ; Génie logiciel ; Python (Langage de programmation) ; Programmation (Informatique) ; computer programming ; Instructional films ; Nonfiction films ; Internet videos ; Films de formation ; Films autres que de fiction ; Vidéos sur Internet
    Abstract: The systems of today are exponentially more complex than the systems of 15, or even 10 years ago. There are way more moving parts and interactions to keep track of, sometimes leading to systems behaving in very unpredictable ways. In the past, Software Engineers and Site Reliability Engineers (SREs) could rely on logging and monitoring to make sense of their systems. This is no longer the case. The good news is that Observability can help. In this course, you will learn about how Observability can help SREs and Software Engineers make sense of what's going on in their systems. You will also learn about OpenTelemetry: what it is, how it supports Observability goals, how OpenTelemetry instrumentation works, and how the OpenTelemetry Collector and OpenTelemetry Operator further enhance OpenTelemetry's capabilities. You will put OpenTelemetry theory into practice with hands-on exercises which include instrumenting a Python application with OpenTelemetry, configuring the OpenTelemetry Collector, and deploying and configuring the OpenTelemetry Kubernetes Operator. Finally, you will learn what pitfalls to avoid when setting up an Observability practice, to ensure that you and your teams are positioned for success, and explore some advanced Observability use cases supported by OpenTelemetry. What you'll learn and how you can apply it Understand what Observability is, and why it is an important practice for SREs and software engineers Understand how OpenTelemetry helps to achieve Observability, and understand the basic building blocks required to instrument an application Understand the value of the OpenTelemetry Collector, and how to configure and deploy it Understand the value of the OpenTelemetry Operator, and how to configure and deploy it Quickly see OpenTelemetry in action in a complex ecosystem by running the OpenTelemetry Demo App Use OpenTelemetry to instrument a simple Python application and send traces to an Observability back-end via the OpenTelemetry Collector Understand what pitfalls to avoid in order to run a successful Observability practice Understand additional ways in which OpenTelemetry can help achieve Observability This course is for you because... You're a Site Reliability Engineer looking to improve the reliability of your systems. You're a Software Engineer looking to improve the debuggability of your code. Prerequisites: Familiarity with Linux Working knowledge of Python programming Docker fundamentals Git fundamentals Kubernetes fundamentals, including deploying applications to Kubernetes.
    Note: Online resource; title from title details screen (O'Reilly, viewed April 2, 2024)
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  • 34
    Online Resource
    Online Resource
    [Sebastopol, California] : O'Reilly Media, Inc.
    ISBN: 9781098171322 , 1098171322
    Language: English
    Pages: 1 online resource (5 pages)
    Edition: [First edition].
    Series Statement: Shortcuts
    DDC: 005.13/3
    Keywords: Python (Computer program language) ; Python (Langage de programmation)
    Abstract: Build your knowledge of Python with this Shortcuts collection. Focusing on common problems involving text manipulation, many of these tasks can be solved using built-in methods of strings. More complicated operations may require regular expressions or the creation of a full-fledged parser.
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  • 35
    ISBN: 9781835462683 , 1835462685 , 9781835464946
    Language: English
    Pages: 1 online resource
    Edition: 1st edition.
    Parallel Title: Erscheint auch als
    DDC: 005.13/3
    Keywords: Machine learning ; Python (Computer program language) ; Data mining ; Apprentissage automatique ; Python (Langage de programmation) ; Exploration de données (Informatique)
    Abstract: Use active machine learning with Python to improve the accuracy of predictive models, streamline the data analysis process, and adapt to evolving data trends, fostering innovation and progress across diverse fields Key Features Learn how to implement a pipeline for optimal model creation from large datasets and at lower costs Gain profound insights within your data while achieving greater efficiency and speed Apply your knowledge to real-world use cases and solve complex ML problems Purchase of the print or Kindle book includes a free PDF eBook Book Description Building accurate machine learning models requires quality data--lots of it. However, for most teams, assembling massive datasets is time-consuming, expensive, or downright impossible. Led by Margaux Masson-Forsythe, a seasoned ML engineer and advocate for surgical data science and climate AI advancements, this hands-on guide to active machine learning demonstrates how to train robust models with just a fraction of the data using Python's powerful active learning tools. You'll master the fundamental techniques of active learning, such as membership query synthesis, stream-based sampling, and pool-based sampling and gain insights for designing and implementing active learning algorithms with query strategy and Human-in-the-Loop frameworks. Exploring various active machine learning techniques, you'll learn how to enhance the performance of computer vision models like image classification, object detection, and semantic segmentation and delve into a machine AL method for selecting the most informative frames for labeling large videos, addressing duplicated data. You'll also assess the effectiveness and efficiency of active machine learning systems through performance evaluation. By the end of the book, you'll be able to enhance your active learning projects by leveraging Python libraries, frameworks, and commonly used tools. What you will learn Master the fundamentals of active machine learning Understand query strategies for optimal model training with minimal data Tackle class imbalance, concept drift, and other data challenges Evaluate and analyze active learning model performance Integrate active learning libraries into workflows effectively Optimize workflows for human labelers Explore the finest active learning tools available today Who this book is for Ideal for data scientists and ML engineers aiming to maximize model performance while minimizing costly data labeling, this book is your guide to optimizing ML workflows and prioritizing quality over quantity. Whether you're a technical practitioner or team lead, you'll benefit from the proven methods presented in this book to slash data requirements and iterate faster. Basic Python proficiency and familiarity with machine learning concepts such as datasets and convolutional neural networks is all you need to get started.
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  • 36
    Online Resource
    Online Resource
    [Place of publication not identified] : O'Reilly Media, Inc.
    Language: English
    Pages: 1 online resource (1 video file (3 hr., 57 min.)) , sound, color.
    Edition: [First edition].
    DDC: 005.13/3
    Keywords: Python (Computer program language) ; Machine learning ; Computer programming ; Python (Langage de programmation) ; Apprentissage automatique ; Programmation (Informatique) ; computer programming ; Instructional films ; Nonfiction films ; Internet videos ; Films de formation ; Films autres que de fiction ; Vidéos sur Internet
    Abstract: In this course, you will learn to use Python for data science and gain the essential skills to analyze and visualize data effectively. Whether you are a data analyst, data scientist, business analyst, or data engineer, this course will provide you with the knowledge and tools to excel in your role. Python is a versatile language that offers powerful libraries for data manipulation, visualization, and machine learning, making it the go-to choice for data professionals. The course solves the problem of understanding and leveraging Python's capabilities for data science by providing business use cases. You will learn to use popular Python libraries such as Pandas, Matplotlib, Seaborn, and Scikit-learn to analyze and visualize data, perform statistical analysis, and build predictive models. By the end of the course, you will have a solid foundation in Python for data science and be ready to apply your skills to real-world projects. What you'll learn and how you can apply it Upon completion of this course, learners will be able to: Apply Python programming concepts for data analysis and visualization Manipulate and analyze data using Pandas Create informative and visually appealing data visualizations using Matplotlib and Seaborn Perform statistical analysis to gain insights from the data Build and evaluate machine learning models for predictive analytics This course is for you because... You're a Python beginner who wants to learn how to manage data with Python. You're a traditional data analyst who has experience with tools like Excel and Tableau, but wants to learn how to manage data with Python. You're a finance, healthcare, ecommerce, or manufacturing/logistics professional looking to become adept in Python and data science. Prerequisites No prior knowledge of Python or Data analytics is needed. All course files can be accessed in this GitHub repository.
    Note: Online resource; title from title details screen (O'Reilly, viewed April 23, 2024)
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  • 37
    Online Resource
    Online Resource
    [Sebastopol, California] : O'Reilly Media, Inc.
    ISBN: 9781098171520 , 1098171527
    Language: English
    Pages: 1 online resource (5 pages)
    Edition: [First edition].
    Series Statement: Shortcuts
    DDC: 005.13/3
    Keywords: Python (Computer program language) ; Python (Langage de programmation)
    Abstract: Build your knowledge of Python, the world's favorite programming language, with this Shortcuts collection. These recipes address a range of well-known and more advanced function definitions, as well as common programming patterns for class and object definitions.
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  • 38
    Online Resource
    Online Resource
    [Sebastopol, California] : O'Reilly Media, Inc.
    ISBN: 9781098171568 , 109817156X
    Language: English
    Pages: 1 online resource (5 pages)
    Edition: [First edition].
    Series Statement: Shortcuts
    DDC: 005.13/3
    Keywords: Python (Computer program language) ; Python (Langage de programmation)
    Abstract: Build your knowledge of Python, the world's favorite programming language, with this Shortcuts collection. These recipes address a range of well-known and more advanced function definitions, as well as common programming patterns for class and object definitions.
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  • 39
    Online Resource
    Online Resource
    [Sebastopol, California] : O'Reilly Media, Inc.
    ISBN: 9781098171544 , 1098171543
    Language: English
    Pages: 1 online resource (5 pages)
    Edition: [First edition].
    Series Statement: Shortcuts
    DDC: 005.13/3
    Keywords: Python (Computer program language) ; Python (Langage de programmation)
    Abstract: Build your knowledge of Python, the world's favorite programming language, with this Shortcuts collection. These recipes address a range of well-known and more advanced function definitions, as well as common programming patterns for class and object definitions.
    Library Location Call Number Volume/Issue/Year Availability
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  • 40
    Online Resource
    Online Resource
    [Sebastopol, California] : O'Reilly Media, Inc.
    ISBN: 9781098171353 , 1098171357
    Language: English
    Pages: 1 online resource (5 pages)
    Edition: [First edition].
    Series Statement: Shortcuts
    DDC: 005.13/3
    Keywords: Python (Computer program language) ; Python (Langage de programmation)
    Abstract: Build your knowledge of Python with this Shortcuts collection. Focusing on common problems involving text manipulation, many of these tasks can be solved using built-in methods of strings. More complicated operations may require regular expressions or the creation of a full-fledged parser.
    Library Location Call Number Volume/Issue/Year Availability
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  • 41
    Online Resource
    Online Resource
    [Sebastopol, California] : O'Reilly Media, Inc.
    ISBN: 9781098171537 , 1098171535
    Language: English
    Pages: 1 online resource (5 pages)
    Edition: [First edition].
    Series Statement: Shortcuts
    DDC: 005.13/3
    Keywords: Python (Computer program language) ; Python (Langage de programmation)
    Abstract: Build your knowledge of Python, the world's favorite programming language, with this Shortcuts collection. These recipes address a range of well-known and more advanced function definitions, as well as common programming patterns for class and object definitions.
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  • 42
    Online Resource
    Online Resource
    Hoboken, New Jersey : John Wiley & Sons, Inc.
    ISBN: 9781394236152
    Language: English
    Pages: 1 online resource (704 pages) , illustrations
    Edition: 3rd edition.
    Series Statement: For dummies
    DDC: 005.13/3
    Keywords: Python (Computer program language) ; Computer programming ; Python (Langage de programmation) ; Programmation (Informatique) ; computer programming
    Abstract: Python All-in-One For Dummies is your one-stop source for answers to all your Python questions. From creating apps to building complex web sites to sorting big data, Python provides a way to get the work done. This book is great as a starting point for those new to coding, and it also makes a perfect reference for experienced coders looking for more than the basics. Apply your Python skills to data analysis, learn to write AI-assisted code using GitHub CoPilot, and discover many more exciting uses for this top programming language.
    Note: Includes index
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  • 43
    Online Resource
    Online Resource
    [Place of publication not identified] : Packt Publishing
    ISBN: 9781836204459 , 1836204450
    Language: English
    Pages: 1 online resource (1 video file (3 hr., 8 min.)) , sound, color.
    Edition: [First edition].
    DDC: 005.1/17
    Keywords: Python (Computer program language) ; Application software Development ; Python (Langage de programmation) ; Logiciels d'application ; Développement ; Instructional films ; Nonfiction films ; Internet videos ; Films de formation ; Films autres que de fiction ; Vidéos sur Internet
    Abstract: """Abstracting with Functions"" is a meticulously curated video course designed for technical professionals eager to master the art of function creation in Python. Through a comprehensive series of videos, learners are taken on a journey that begins with the basics of what functions are and why they are pivotal in programming. The course underscores the significance of functions in collecting logic into reusable chunks, thereby fostering more readable and maintainable codebases. Starting with an introductory overview, the course swiftly moves to dissect the anatomy of functions, from the simplest forms without arguments to more complex functions featuring multiple positional and keyword arguments. Each episode, hosted by dynamic duos such as Aubri and Ronnie or Justin and Zach, not only discusses the syntax and theoretical aspects but also delves into practical demonstrations and the subtleties of function creation, such as edge cases and argument types. The importance of understanding functions is also linked to professional development, highlighting their relevance in networking and certification exams. By the end of the course, learners will have a solid grasp of how to efficiently abstract logic through functions, enabling the construction of more sophisticated and scalable programs. What you will learn Understand the fundamental concept of functions. Explore the syntax and creation of functions in Python. Distinguish between various types of functions. Learn advanced topics such as keyword arguments. Use mixed arguments to enhance function versatility. Grasp the significance of functions in different contexts. Audience Individuals at the beginning of their coding journey who want to learn best practices early on, and those with some experience in Python or other programming languages looking to deepen their understanding of functions and how to use them more effectively in their projects will find this course quite useful. About the Authors ACI Learning: ACI Learning trains leaders in Cybersecurity, Audit, and Information Technology. Whether starting an IT career, mastering a profession, or developing a team, they provide essential support at every step. Justin Dennison: Justin leads AWS and developer content creation for ITProTV. He has experience in multiple programming languages. His certifications include AWS Certified Solutions Architect -- Associate, AWS Certified Developer -- Associate, AWS Certified Cloud Practitioner, Oracle Certified Associate, Java SE 7 Programmer. Zachary Memos: Zachary Memos is a show host with 30+ years of on-camera experience. He brings his wit and charm to the camera while helping bring out the best in the ITProTV subject matter experts.".
    Note: Online resource; title from title details screen (O'Reilly, viewed April 23, 2024)
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  • 44
    Online Resource
    Online Resource
    Hoboken, New Jersey : Wiley
    ISBN: 9781394213269 , 1394213263 , 9781394213252 , 1394213255 , 9781394213276 , 1394213271 , 9781394213245
    Language: English
    Pages: 1 online resource
    Parallel Title: Erscheint auch als
    DDC: 006.3/12
    Keywords: Data mining ; R (Computer program language) ; Python (Computer program language) ; Exploration de données (Informatique) ; R (Langage de programmation) ; Python (Langage de programmation)
    Abstract: "Data science is an interdisciplinary academic field that uses statistics, scientific computing, scientific methods, processes, algorithms and systems to extract or extrapolate knowledge and insights from noisy, structured, and unstructured data."--
    Note: Includes index. - Description based on print version record and CIP data provided by publisher; resource not viewed
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  • 45
    Online Resource
    Online Resource
    [Place of publication not identified] : Packt Publishing
    ISBN: 9781836206071 , 1836206070
    Language: English
    Pages: 1 online resource (1 video file (2 hr., 27 min.)) , sound, color.
    Edition: [First edition].
    DDC: 005.13/3
    Keywords: ChatGPT ; Python (Computer program language) ; Computer programming ; Artificial intelligence ; Python (Langage de programmation) ; Programmation (Informatique) ; Intelligence artificielle ; computer programming ; artificial intelligence ; Instructional films ; Nonfiction films ; Internet videos ; Films de formation ; Films autres que de fiction ; Vidéos sur Internet
    Abstract: "Unlock the world of conversational AI with ""Create Your Own ChatGPT Clone using Python,"" a comprehensive video course designed to teach you the intricacies of AI model interactions. Starting with a solid introduction and setup requirements, this course swiftly moves into hands-on exercises where you'll engage with the ChatGPT API using Python. You'll not only learn about API parameters and endpoints but also how to weave these components into a responsive Python FastAPI service. Transitioning from Python to front-end development, the course empowers you to create a React project, giving life to your ChatGPT clone with engaging styles and functionalities. The journey continues into the nuances of fine-tuning models, where you'll tailor ChatGPT solutions to fit specific needs, offering personalized experiences. The course culminates in strategic model selection, ensuring you're equipped to choose the most effective GPT model for your project's requirements, capped off with a cohesive summary tying all learned concepts together. What you will learn Navigate the setup for ChatGPT API interaction. Create a Python FastAPI for ChatGPT. Construct and style a ChatGPT clone in a React environment. Customize ChatGPT models through fine-tuning techniques. Select the optimal GPT model for specific project needs. Synthesize course knowledge in a comprehensive project. Audience Geared towards Python developers keen on building AI chatbots, and AI and NLP enthusiasts looking to delve into chatbot development, this course suits students and professionals eager for hands-on AI and Python experience. A foundational grasp of Python, React, and basic API interactions is assumed, catering to innovators who want to unlock the potential of conversational AI. About the Author Eduonix Learning Solutions: Eduonix Learning Solutions is at the forefront of technology training, boasting a vast global reach with over 1 million students across 200+ courses. With a rich history spanning over a decade, Eduonix's mission is to deliver industry-standard, high-quality training content created by a team of seasoned industry professionals. Their comprehensive curriculum covers a wide range of technologies, from Web and Mobility to Enterprise, Database, and Server Administration. Eduonix is dedicated to teaching technology in the context of real-world professional applications, ensuring that learners are equipped with the skills needed to excel in today's tech-driven landscape.".
    Note: Online resource; title from title details screen (O'Reilly, viewed April 22, 2024)
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  • 46
    ISBN: 9781394220625 , 1394220626 , 1394220634 , 9781394220649 , 1394220642 , 9781394220632 , 9781394220618
    Language: English
    Pages: 1 online resource (512 pages) , illustrations (some color)
    Parallel Title: Erscheint auch als
    DDC: 006.3/1
    Keywords: Machine learning ; Quantum computing ; Python (Computer program language) ; Apprentissage automatique ; Informatique quantique ; Python (Langage de programmation) ; Machine learning ; Python (Computer program language) ; Quantum computing
    Abstract: "Machine learning (ML) and quantum computing are two technologies that have the potential to allow us to solve complex, previously impossible problems and help speed up areas such as model training or pattern recognition. The future of computing will certainly be comprised of classical, biologically inspired, and quantum computing. The intersection between quantum computing and AI/ML has received considerable attention in recent years and has enabled the development of quantum machine learning algorithms such as quantum-enhanced Support Vector Machines (QSVMs), QSVM multiclass classification, variational quantum classifiers or quantum generative adversarial networks (qGANs)."--
    Note: Includes bibliographical references and index. - Description based on online resource; title from digital title page (viewed on March 26, 2024)
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  • 47
    ISBN: 9781633437784 , 1633437787
    Language: English
    Pages: 1 online resource (296 pages) , illustrations
    Edition: [First edition].
    Parallel Title: Erscheint auch als Porter, Leo Learn AI-assisted Python programming
    DDC: 005.13/3
    Keywords: ChatGPT ; Python (Computer program language) ; Computer programming ; Natural language processing (Computer science) ; Artificial intelligence Computer programs ; Python (Langage de programmation) ; Programmation (Informatique) ; Traitement automatique des langues naturelles ; Intelligence artificielle ; Logiciels ; computer programming ; Einführung ; Künstliche Intelligenz ; Programmierung ; Python
    Abstract: Writing computer programs in Python just got a lot easier! Use AI-assisted coding tools like GitHub Copilot and ChatGPT to turn your ideas into applications faster than ever. AI has changed the way we write computer programs. With tools like Copilot and ChatGPT, you can describe what you want in plain English, and watch your AI assistant generate the code right before your eyes. It’s perfect for beginners, or anyone who’s struggled with the steep learning curve of traditional programming. Learn AI-Assisted Python Programming: With GitHub Copilot and ChatGPT is a hands-on beginner’s guide that is written by two esteemed computer science university professors. It teaches you everything you need to start programming Python in an AI-first world. You’ll hit the ground running, writing prompts that tell your AI-assistant exactly what you want your programs to do. Along the way, you’ll pick up the essentials of Python programming and practice the higher-level thinking you’ll need to create working apps for data analysis, automating tedious tasks, and even video games. The way people write computer programs has changed forever. Using GitHub Copilot, you describe in plain English what you want your program to do, and the AI generates it instantly. This book shows you how to create and improve Python programs using AI—even if you’ve never written a line of computer code before. Spend less time on the slow, low-level programming details and instead learn how an AI assistant can bring your ideas to life immediately. As you go, you’ll even learn enough of the Python language to understand and improve what your AI assistant creates.
    Note: Includes bibliographical references and index
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  • 48
    Online Resource
    Online Resource
    Shelter Island, NY : Manning Publications
    ISBN: 9781633438453 , 1633438457 , 9781638354369 , 1638354367
    Language: English
    Pages: 1 online resource (248 pages) , illustrations
    Edition: [First edition].
    Parallel Title: Erscheint auch als
    DDC: 629.8/92
    Keywords: Robotics ; Python (Computer program language) ; Raspberry Pi (Computer) ; Robotique ; Python (Langage de programmation) ; Raspberry Pi (Ordinateur)
    Abstract: Build Your Own Robot is a project-based guide that takes you from spinning your first DC motor to programming a mobile robot that you can control from your phone or computer. You’ll write simple Python code to help your new friend spin, move, and find its way. You’ll even teach it to track faces and fetch snacks. Plus, a helpful hardware purchasing guide makes it easy to find exactly what you need to get started!
    Note: Includes index
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  • 49
    Online Resource
    Online Resource
    San Francisco : No Starch Press
    Language: English
    Pages: 1 online resource (352 pages) , illustrations
    Edition: Second edition.
    Parallel Title: Erscheint auch als
    DDC: 005.13/3
    Keywords: Python (Computer program language) ; Electronic apparatus and appliances Automatic control ; Arduino (Programmable controller) Programming ; Raspberry Pi (Computer) Programming
    Abstract: Harness the power of Python as you turn code into tangible creations with Python Playground, a collection of 15 inventive projects that will expand your programming horizons, spark your curiosity, and elevate your coding skills. Go beyond the basics as you write programs to generate art and music, simulate real-world phenomena, and interact with hardware, all through the use of Python and common libraries such as numpy, matplotlib, and Pillow. New to this edition: We’ve expanded your playground with five new projects: you’ll draw fractals, bring Conway’s Game of Life into 3D space, and use a Raspberry Pi and Python to create a musical instrument, an IoT garden monitor, and even a machine learning–driven speech recognition system. Whether you’re a seasoned professional or just getting started, you’ll find Python Playground to be a great way to learn, experiment with, and master this versatile programming language. Covers Python 3.x
    Note: Includes index. - Description based on print version record
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  • 50
    Online Resource
    Online Resource
    [Erscheinungsort nicht ermittelbar] : JOHN WILEY & SONS
    ISBN: 9781394213085 , 1394213085 , 9781394213146
    Language: English
    Pages: 1 online resource
    Series Statement: For dummies
    Parallel Title: Erscheint auch als
    DDC: 005.13/3
    Keywords: Python (Computer program language) ; Programming languages (Electronic computers) ; Data mining
    Abstract: Python for Data Science For Dummies lets you get your hands dirty with data using one of the top programming languages. This beginner's guide takes you step by step through getting started, performing data analysis, understanding datasets and example code, working with Google Colab, sampling data, and beyond. Coding your data analysis tasks will make your life easier, make you more in-demand as an employee, and open the door to valuable knowledge and insights. This new edition is updated for the latest version of Python and includes current, relevant data examples.
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  • 51
    Online Resource
    Online Resource
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc.
    ISBN: 9781098171377 , 1098171373
    Language: Undetermined
    Pages: 1 online resource
    DDC: 005.13/3
    Keywords: Python (Computer program language) ; Web site development ; Python (Langage de programmation) ; Sites Web ; Développement
    Abstract: Build your knowledge of Python with this Shortcuts collection. Focusing on common problems involving text manipulation, many of these tasks can be solved using built-in methods of strings. More complicated operations may require regular expressions or the creation of a full-fledged parser.
    Note: Machine-generated record
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  • 52
    Online Resource
    Online Resource
    [Erscheinungsort nicht ermittelbar] : John Wiley
    ISBN: 9781394263486 , 1394263481 , 9781394263479
    Language: English
    Pages: 1 online resource
    Series Statement: For dummies
    Parallel Title: Erscheint auch als
    DDC: 005.13/3
    Keywords: Python (Computer program language) ; Computer programming ; Python (Langage de programmation) ; Programmation (Informatique) ; computer programming
    Abstract: Python Essentials For Dummies is a quick reference to all the core concepts in Python, the multifaceted general-purpose language used for everything from building websites to creating apps. This book gets right to the point, with no excess review, wordy explanations, or fluff, making it perfect as a desk reference on the job or as a brush-up as you expand your skills in related areas. Focusing on just the essential topics you need to know to brush up or level up your Python skill, this is the reliable little book you can always turn to for answers.
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  • 53
    Online Resource
    Online Resource
    [Place of publication not identified] : Packt Publishing
    ISBN: 9781837630875 , 1837630879
    Language: English
    Pages: 1 online resource (1 video file (2 hr., 39 min.)) , sound, color.
    Edition: [First edition].
    DDC: 005.13/3
    Keywords: Python (Computer program language) ; Social media ; Computer programming ; Instructional films ; Nonfiction films ; Internet videos
    Abstract: Welcome to this course on automating social media applications using Python. Let's learn the basic concepts required for automating our routine social media tasks. We will begin with learning to automate WhatsApp and explore how to automatically send messages to individuals and groups using Python. You will learn to automate sending emails to random users with just a few lines of code. Learn to schedule tasks using Python to implement different social media projects. Explore email automation with Smtplib and link Gmail with SMTP. We will explore how to fetch and download different stats from YouTube channels as CSV or other file formats. Finally, you will learn to post various types of content on Facebook pages, delete posts, and comment on posts. The course, however, does not cover Python programming lessons separately. Upon completing the course, you can automate social media platforms such as WhatsApp, YouTube, Facebook, and Gmail. Create automated posts and comments, post and delete content, schedule tasks, and comprehensively advance your social media automation skills. What You Will Learn Automate WhatsApp, install the toolkit, schedule and send messages Link Gmail with SMTP, create email, attach files and send Schedule, install, import, and code a simple scheduler Create a YouTube API key and fetch channel and playlist information Automate Facebook, post photos with captions, and comment on a post Understand and implement automation packages and libraries Audience This course is designed for beginners with some programming experience or even those who need to know about data analysis, data manipulation, and more. This course is for individuals wanting to understand and implement automation packages and libraries, master scheduling tasks, automate emails, automate WhatsApp, automate YouTube data, and automate Facebook posts. Basic to intermediate Python programming knowledge is required for the course. About The Author AI Sciences: AI Sciences is a group of experts, PhDs, and practitioners of AI, ML, computer science, and statistics. Some of the experts work in big companies such as Amazon, Google, Facebook, Microsoft, KPMG, BCG, and IBM. They have produced a series of courses mainly dedicated to beginners and newcomers on the techniques and methods of machine learning, statistics, artificial intelligence, and data science. Initially, their objective was to help only those who wish to understand these techniques more easily and to be able to start without too much theory. Today, they also publish more complete courses for a wider audience. Their courses have had phenomenal success and have helped more than 100,000 students master AI and data science.
    Note: "Published in January 2023.". - Online resource; title from title details screen (O'Reilly, viewed February 20, 2023)
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  • 54
    Online Resource
    Online Resource
    [Place of publication not identified] : Packt Publishing
    ISBN: 9781837632510 , 1837632510
    Language: English
    Pages: 1 online resource (1 video file (12 hr., 26 min.)) , sound, color.
    Edition: [First edition].
    DDC: 519.55
    Keywords: Time-series analysis Data processing ; Time-series analysis Computer programs ; Python (Computer program language)
    Abstract: Have you ever wondered how weather predictions, population estimates, and even the lifespan of the universe are made? Discover the power of time series forecasting with state-of-the-art ML and DL models. The course begins with the fundamentals of time series analysis, including its characteristics, applications in real-world scenarios, and practical examples. Then progress to exploring data analysis and visualization techniques for time series data, ranging from basic to advanced levels, using powerful libraries such as NumPy, Pandas, and Matplotlib. Python will be utilized to assess various aspects of your time series data, such as seasonality, trend, noise, autocorrelation, mean over time, correlation, and stationarity. Additionally, you will learn how to pre-process time series data for utilization in applied machine learning and recurrent neural network models, which will enable you to train, test, and assess your forecasted results. Finally, you will acquire an understanding of the applied ML models, including their performance evaluations and comparisons. In the RNNs module, you will be building GRU, LSTM, Stacked LSTM, BiLSTM, and Stacked BiLSTM models. By the end of this course, you will be able to understand time series forecasting and its parameters, evaluate the ML models, and evaluate the model and implement RNN models for time series forecasting. What You Will Learn Learn data analysis techniques and handle time series forecasting Implement data visualization techniques using Matplotlib Evaluate applied machine learning in time series forecasting Look at auto regression, ARIMA, Auto ARIMA, SARIMA, and SARIMAX Learn to model LSTM, Stacked LSTM, BiLSTM, and Stacked BiLSTM models Implement ML and RNN models with three state-of-the-art projects Audience No prior knowledge of DL, data analysis, or math is required. You will start from the basics and gradually build your knowledge of the subject. Only the basics of Python are required. This course is designed for both beginners with some programming experience and even those who know nothing about data analysis, ML, and RNNs. The course is suitable for individuals who want to advance their skills in ML and DL, master the relation of data science with time series analysis, implement time series parameters and evaluate their impact on it and implement ML algorithms for time series forecasting. About The Author AI Sciences: AI Sciences are experts, PhDs, and artificial intelligence practitioners, including computer science, machine learning, and Statistics. Some work in big companies such as Amazon, Google, Facebook, Microsoft, KPMG, BCG, and IBM. AI sciences produce a series of courses dedicated to beginners and newcomers on techniques and methods of machine learning, statistics, artificial intelligence, and data science. They aim to help those who wish to understand techniques more easily and start with less theory and less extended reading. Today, they publish more comprehensive courses on specific topics for wider audiences. Their courses have successfully helped more than 100,000 students master AI and data science.
    Note: "Published in March 2022."
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  • 55
    ISBN: 9788383220703 , 8383220707
    Language: Polish
    Pages: 1 online resource (192 pages) , illustrations
    Edition: [First edition].
    Uniform Title: Advanced analytics with PySpark
    DDC: 006.3/12
    Keywords: SPARK (Electronic resource) ; Data mining ; Big data ; Python (Computer program language)
    Abstract: Potrzeby w zakresie analizy dużych zbiorów danych i wyciągania z nich użytecznych informacji stale rosną. Spośród dostępnych narzędzi przeznaczonych do tych zastosowań szczególnie przydatny jest PySpark - interfejs API systemu Spark dla języka Python. Apache Spark świetnie się nadaje do analizy dużych zbiorów danych, a PySpark skutecznie ułatwia integrację Sparka ze specjalistycznymi narzędziami PyData. By jednak można było w pełni skorzystać z tych możliwości, konieczne jest zrozumienie interakcji między algorytmami, zbiorami danych i wzorcami używanymi w analizie danych. Oto praktyczny przewodnik po wersji 3.0 systemu Spark, metodach statystycznych i rzeczywistych zbiorach danych. Omówiono w nim zasady rozwiązywania problemów analitycznych za pomocą interfejsu PySpark, z wykorzystaniem dobrych praktyk programowania w systemie Spark. Po lekturze można bezproblemowo zagłębić się we wzorce analityczne oparte na popularnych technikach przetwarzania danych, takich jak klasyfikacja, grupowanie, filtrowanie i wykrywanie anomalii, stosowane w genomice, bezpieczeństwie systemów IT i finansach. Dodatkowym plusem są opisy wykorzystania przetwarzania obrazów i języka naturalnego. Zaletą jest też szereg rzeczywistych przykładów dużych zbiorów danych i ich zaawansowanej analizy.
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  • 56
    Online Resource
    Online Resource
    [Place of publication not identified] : Manning Publications
    Language: English
    Pages: 1 online resource (1 video file (18 hr., 9 min.)) , sound, color.
    Edition: [First edition].
    DDC: 005.13/3
    Keywords: Python (Computer program language) Study guides ; Computer programming Study guides ; Computer programming ; Python (Computer program language) ; Instructional films ; Internet videos ; Nonfiction films ; Study guides ; Instructional films ; Nonfiction films ; Internet videos
    Abstract: This is the course for people who want to get started programming with Python, and are interested in entering the fields of coding or data science. But it is comprehensive enough for more experienced Python coders who want to brush up on their skills or fill in missing gaps. With over 18 hours of HD video tutorials, this course is designed to fully immerse you in the Python language as you start from the basics of programming and go through to advanced Python concepts. We'll also cover web scraping, PyMongo, WebPy development, Django web framework, GUI programming, data visualization, machine learning, and much more. Get hands-on practice building these ten coding projects: Simple calculator RPG battle script Web scraper PyMongo database Webpy web app Django website PyQt calculator GUI PyQt web browser Data visualization with matplotlib and Pandas Speech recognition & AI.
    Note: Online resource; title from title details screen (O'Reilly, viewed June 26, 2023)
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  • 57
    Online Resource
    Online Resource
    [Place of publication not identified] : Addison-Wesley Professional
    ISBN: 9780138050764 , 0138050767
    Language: English
    Pages: 1 online resource (1 video file (7 hr., 31 min.)) , sound, color.
    Edition: [First edition].
    Series Statement: Live lessons
    DDC: 005.13/3
    Keywords: Python (Computer program language) ; Computer programming ; Computer programming ; Python (Computer program language) ; Instructional films ; Internet videos ; Nonfiction films ; Instructional films ; Nonfiction films ; Internet videos
    Abstract: 7.5 Hours of Video Instruction In Learn Enough Python to Be Dangerous: A Tutorial Introduction to Programming with Python, renowned instructor Michael Hartl teaches you to write practical and modern programs using the elegant and powerful Python programming language. Overview Programmers love Python for its clean syntax, flexible data types, a wealth of useful libraries, and a powerful and elegant design that supports multiple styles of programming. That's why it is popular for varied uses such as scripting, web development, and data science. You'll love Python too, but you don't need to learn "everything" about it, just how to use it efficiently to solve real problems. Best-selling author Michael Hartl gets you started writing practical and modern Python programs as fast as possible, with a focus on the real tools used every day by software developers. You'll learn how to use Python interactively, write shell scripts in it, use Python and a web framework to make simple dynamic web applications, and use Python libraries to do data science. Even if you're new to programming, Hartl helps you quickly build technical sophistication as you gain a solid understanding of object-oriented and functional programming, develop and publish a Python web application with the Flask framework. Focused exercises help you internalize what matters, without wasting time on details pros don't care about. Soon, it'll be like you were born knowing this stuff--and you'll be suddenly, seriously dangerous. About the Instructor Michael Hartl is the creator of the Python on Rails Tutorial, one of the leading introductions to web development, and is cofounder and principal author at Learn Enough. Previously, he was a physics instructor at the California Institute of Technology (Caltech), where he received a Lifetime Achievement Award for Excellence in Teaching. He is a graduate of Harvard College, has a Ph.D. in Physics from Caltech, and is an alumnus of the Y Combinator entrepreneur program. Skill Level Beginner to intermediate Learn How To Create a simple "hello, world" program using several different techniques Deploy a simple dynamic Python application to the web Use strings, arrays, and other native objects Define functions Use Python for functional and object-oriented programming Utilize test-driven development Write a shell script Develop a full Python web application for detecting palindromes Who Should Take This Course New and experienced developers looking for a practical introduction to Python. Course Requirements The only prerequisites are a familiarity with basic developer tools (command line, text editor, and Git) and beginning HTML Some programming experience is useful but not required Lesson Descriptions Lesson 1: Hello World! Lesson 1 begins at the beginning by having you create four simple "hello, world" programs using several different techniques. The main purpose of the "hello, world" is to make sure your system is correctly configured to execute the simple program that prints the string "hello, world!" to the screen. You start by writing a series of programs to display a greeting at a command line terminal, first in a REPL, then from a file, and then from a shell script. Finally, you write and deploy a simple proof-of-concept web application using the Flask web framework. Lesson 2: Strings Lesson 2 covers strings, probably the most important data structure on the Web since Web pages ultimately consist of strings and characters sent to and from the browser. Many other kinds of programs require string manipulation as well. As a result, strings make a great place to start your Python programming journey. The lesson starts with what strings are and how to create them. You then learn how to join, or concatenate, multiple string into a single string. Then you learn how to insert or interpolate one string into another. Next you learn how to print strings to the screen from the terminal window. As part of this, you see your first examples of Python Boolean variables and control flow. Finally, you learn how to iterate over strings with for loops, enabling you to access strings one character at a time. Lesson 3 : Lists In Lesson 2, you learned that strings can be thought of sequences of characters in a particular order. In Lesson 3, you learn about the list data type, which is the general Python container for arbitrary elements in a particular order. You start by explicitly connecting strings and lists via the string split method, and then you learn about various list methods throughout the rest of the lesson. After learning to split strings, you learn how to access elements in the resulting list, discovering that the same syntax works on strings, further deepening the connection between the two data types. Next you learn a variety of additional list methods beginning with selecting both single elements and multiple elements at once using list slicing, including the useful range datatype, and a clever technique using negative indices to select the last element in a list. Then you learn how to sort lists, which, if you have ever written a sorting algorithm by hand, you will find Python makes it ridiculously easy. You also learn how to reverse lists, a capability you will put to good use later on in the tutorial when learning to detect palindromes. Next you will learn how to add and remove list elements using append and pop. You then learn how to undo a string split using a list join, which includes an introduction to an important technique known as generator comprehension. Next, you learn how to iterate through lists using the same kind of for loop covered in Lesson in 2, which is valuable preparation for more advanced techniques covered in Lesson 6. Finally, you learn about two data types closely related to lists: tuples, which are essentially immutable lists, and sets, which can be thought of as a list of elements where repeat elements are ignored and the order doesn't matter. Lesson 4: Other Native Objects Now that we have taken a look at strings and arrays, Lesson 4 continues with a tour of some other important Python objects, which will give you a chance to learn about math, dates, regular expressions, and dictionaries. Like most programming languages, Python supports a large number of mathematical operations right out of the box, such as addition, subtraction, multiplication, and division. It also includes a math library, so you learn about more advanced operations such as logarithms and trigonometric functions. You also see an example of a personal triumph of mine, the inclusion of the circle constant Ï⁴ (tau), to find us the ratio of a circle's circumference to its radius, which Michael first proposed in 2010 and which was added to Python's standard math library in 2017. You also learn how to deal with times and dates in Python, such as getting the year, the day, or the exact time. Next you get an introduction in the powerful subject of regular expressions, which were discussed briefly in Learn Enough Developer Tools to Be Dangerous in the context of text editors and the grep command. Often called regexes for short, regular expressions are a powerful mini-language for matching patterns in text. You learn how to use regexes to quickly search strings for things like five digits in a row, thereby matching standard United States ZIP codes. The lesson ends with an introduction to dictionaries in Python. You use such objects, often referred to as hashes or associative arrays in other languages, are defined by key-value pairs, and in many ways behave like lists with strings, or sometimes other objects, instead of integers as indices. You apply this important object type to write your first substantial Python program, a shell script to count the unique words in a text. Lesson 5: Functions and Iterators So far in this tutorial, Python functions have been mentioned repeatedly, and and in Lesson 5 you finally learn to define functions of your own. The resulting ability gives us greater flexibility as programmers. We begin your study of functions in the read-eval-print loop, that is, the REPL, and then you learn how to put your function definitions in a file for use in a simple Flask web application. The lesson ends with a discussion of iterators, which are a powerful Python object type that represents a stream of data. The lesson pays particular attention to generators, probably the most common type of Python iterator. You will use a generator to make a first definition of an ispalindrome function, to see if a string is the same forward and backward. Lesson 6: Functional Programming Having learned how to define functions and apply them in a couple of different contexts. In Lesson 6, you take your programming to the next level by learning the basics of functional programming, a style of programming that emphasizes, you guessed it, functions. As you will see, functional programming in Python frequently employs a powerful and very Pythonic class of techn...
    Note: Online resource; title from title details screen (O'Reilly, viewed March 21, 2023)
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  • 58
    ISBN: 9781837637263 , 1837637261 , 9781837632749
    Language: English
    Pages: 1 online resource (422 pages) , illustrations
    Edition: Second edition.
    DDC: 005.13/3
    Keywords: Python (Computer program language) ; Data mining
    Abstract: Learn all the features and best practices of FastAPI to build, deploy, and monitor powerful data science and AI apps, like object detection or image generation. Purchase of the print or Kindle book includes a free PDF eBook Key Features Uncover the secrets of FastAPI, including async I/O, type hinting, and dependency injection Learn to add authentication, authorization, and interaction with databases in a FastAPI backend Develop real-world projects using pre-trained AI models Book Description Building Data Science Applications with FastAPI is the go-to resource for creating efficient and dependable data science API backends. This second edition incorporates the latest Python and FastAPI advancements, along with two new AI projects - a real-time object detection system and a text-to-image generation platform using Stable Diffusion. The book starts with the basics of FastAPI and modern Python programming. You'll grasp FastAPI's robust dependency injection system, which facilitates seamless database communication, authentication implementation, and ML model integration. As you progress, you'll learn testing and deployment best practices, guaranteeing high-quality, resilient applications. Throughout the book, you'll build data science applications using FastAPI with the help of projects covering common AI use cases, such as object detection and text-to-image generation. These hands-on experiences will deepen your understanding of using FastAPI in real-world scenarios. By the end of this book, you'll be well equipped to maintain, design, and monitor applications to meet the highest programming standards using FastAPI, empowering you to create fast and reliable data science API backends with ease while keeping up with the latest advancements. What you will learn Explore the basics of modern Python and async I/O programming Get to grips with basic and advanced concepts of the FastAPI framework Deploy a performant and reliable web backend for a data science application Integrate common Python data science libraries into a web backend Integrate an object detection algorithm into a FastAPI backend Build a distributed text-to-image AI system with Stable Diffusion Add metrics and logging and learn how to monitor them Who this book is for This book is for data scientists and software developers interested in gaining knowledge of FastAPI and its ecosystem to build data science applications. Basic knowledge of data science and machine learning concepts and how to apply them in Python is recommended.
    Note: Includes index
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  • 59
    Online Resource
    Online Resource
    [Place of publication not identified] : Packt Publishing
    ISBN: 9781805127598 , 1805127594
    Language: English
    Pages: 1 online resource (1 video file (3 hr., 42 min.)) , sound, color.
    Edition: [First edition].
    DDC: 005.13/3
    Keywords: Python (Computer program language) ; Information visualization ; Computer programming ; Instructional films ; Nonfiction films ; Internet videos
    Abstract: Python-based data visualization uses the Python programming language and its libraries to transform data into visual representations, such as charts, graphs, and interactive dashboards. Python's libraries, including Matplotlib, Seaborn, Plotly, and Bokeh, offer customizable plot types and interactive features to craft compelling visual narratives. Through data storytelling and customization, Python shares insights and effectively communicates them, making it an indispensable skill for anyone working with data. In this course, we will begin by grasping the importance of data visualization and exploring essential Python libraries such as Matplotlib, Seaborn, and Plotly. You will learn to customize and enhance visualizations, adjust colors, labels, and legends, and understand the principles of effective data storytelling. The course delves into advanced topics such as creating interactive dashboards and dynamic data plots. We will work on practical projects and real-world examples to equip us with the skills to turn raw data into informative visuals using Python. Upon completion, we will master Python-based data visualization from core principles to practical skills, Matplotlib, Seaborn, and Plotly, and transform raw data into compelling visuals. We will acquire tools to create visuals, convey insights, and make data-driven decisions with confidence. What You Will Learn Understand the importance/principles of effective data visualization Learn Matplotlib, Seaborn, and Plotly to create various visualizations Learn to tailor colors, labels, and styles to enhance visuals Craft data visualizations to create compelling narratives Create engaging and user-friendly interactive data displays Explore geospatial data mapping and location-based visualizations Audience This course caters to a wide audience from beginners with no programming experience to experienced data professionals, programmers looking to expand their skillsets, business professionals seeking practical data visualization knowledge, and students/researchers aiming to strengthen their data visualization proficiency using Python. There are no specific prerequisites for this course. However, having a basic understanding of mathematics and readiness to learn are helpful attributes for successfully completing the course. About The Author Meta Brains: Meta Brains is a professional training brand developed by a team of software developers and finance professionals who have a passion for finance, coding, and Excel. They bring together both professional and educational experiences to create world-class training programs accessible to everyone. Currently, they're focused on the next great revolution in computing: The Metaverse. Their ultimate objective is to train the next generation of talent so that we can code and build the metaverse together!.
    Note: "Updated for September 2023.". - Online resource; title from title details screen (O'Reilly, viewed October 11, 2023)
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  • 60
    Online Resource
    Online Resource
    [Shelter Island, New York] : Manning Publications
    Language: English
    Pages: 1 online resource (1 audio file (8 hr., 41 min.))
    Edition: [First edition].
    DDC: 005.13/3
    Keywords: Python (Computer program language) ; Electronic data processing ; Audiobooks
    Abstract: Master Python techniques and libraries to reduce run times, efficiently handle huge datasets, and optimize execution for complex machine learning applications. Fast Python is a toolbox of techniques for high performance Python including: Writing efficient pure-Python code Optimizing the NumPy and pandas libraries Rewriting critical code in Cython Designing persistent data structures Tailoring code for different architectures Implementing Python GPU computing Fast Python is your guide to optimizing every part of your Python-based data analysis process, from the pure Python code you write to managing the resources of modern hardware and GPUs. You'll learn to rewrite inefficient data structures, improve underperforming code with multithreading, and simplify your datasets without sacrificing accuracy. Written for experienced practitioners, this book dives right into practical solutions for improving computation and storage efficiency. You'll experiment with fun and interesting examples such as rewriting games in Cython and implementing a MapReduce framework from scratch. Finally, you'll go deep into Python GPU computing and learn how modern hardware has rehabilitated some former antipatterns and made counterintuitive ideas the most efficient way of working. About the Technology Face it. Slow code will kill a big data project. Fast pure-Python code, optimized libraries, and fully utilized multiprocessor hardware are the price of entry for machine learning and large-scale data analysis. What you need are reliable solutions that respond faster to computing requirements while using less resources, and saving money. About the Book Fast Python is a toolbox of techniques for speeding up Python, with an emphasis on big data applications. Following the clear examples and precisely articulated details, you'll learn how to use common libraries like NumPy and pandas in more performant ways and transform data for efficient storage and I/O. More importantly, Fast Python takes a holistic approach to performance, so you'll see how to optimize the whole system, from code to architecture_._ What's Inside Rewriting critical code in Cython Designing persistent data structures Tailoring code for different architectures Implementing Python GPU computing About the Reader For intermediate Python programmers familiar with the basics of concurrency. About the Author Tiago Antao is one of the co-authors of Biopython, a major bioinformatics package written in Python. Quotes.
    Note: Online resource; title from title details screen (O'Reilly, viewed November 15, 2023)
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  • 61
    Online Resource
    Online Resource
    [Place of publication not identified] : Packt Publishing
    ISBN: 9781835464427 , 1835464424
    Language: English
    Pages: 1 online resource (1 video file (10 hr., 2 min.)) , sound, color.
    Edition: [First edition].
    DDC: 004.6
    Keywords: Artificial intelligence ; Computer programming ; Chatbots ; Application program interfaces (Computer software) ; Python (Computer program language) ; Instructional films ; Nonfiction films ; Internet videos
    Abstract: Unlock the boundless possibilities of AI and language-based applications with our LangChain Masterclass. In this comprehensive course, you will embark on a transformative journey through the realms of LangChain, Pinecone, OpenAI, and LLAMA 2 LLM, guided by experts in the field. In this course, you will embark on a journey through a diverse range of projects designed to deepen your understanding and application of cutting-edge technologies. These hands-on endeavors encompass a broad spectrum of applications, from creating dynamic question-answering applications powered by LangChain, OpenAI, and Hugging Face Spaces, to developing engaging conversational bots that enhance user interactions. You will even venture into the realm of educational AI, crafting customized experiences for children. As you progress, you will build captivating marketing campaigns, explore the world of summarization-enriched chatbots, and streamline tasks such as multiple-choice quiz creation and CSV data analysis. Plus, you will discover how to optimize HR processes, simplify email customization, and extract vital invoice details. With projects spanning from text-to-SQL query assistance to customer care call summaries, this course equips you with a comprehensive toolkit for advancing your skills and revolutionizing various domains of AI and software development. By the end of this course, you will not only have a strong grasp of LangChain's capabilities but also a robust portfolio of AI applications that showcases your expertise. What You Will Leanr Build AI-powered chatbots and applications with LangChain Create dynamic question-answering systems and conversational bots Implement automated marketing and customer support tools Learn to streamline data analysis and CSV processing Explore HR resume screening and email customization Master invoice data extraction and SQL query tools Audience This course is designed for individuals eager to explore the dynamic world of AI-powered language applications. If you are passionate about harnessing the potential of LangChain, Pinecone, OpenAI, and LLAMA 2 LLM, this course is your gateway to expertise. Prerequisites are minimal, requiring only a basic understanding of programming and coding. A curious mind and enthusiasm for AI are your most valuable assets. About The Author Sharath Raju: Sharath Raju is a senior software engineer specialized in AI and robotics. It has been over eight years since he worked in software development, robotic process automation (RPA), and AI app implementation. He has implemented over 80 RPA processes using UiPath and Microsoft Power Automate and has also built several AI-powered apps using different technologies. It is so true that someone learns more efficiently by practicing the skill than just reading something. Having a passion to share his knowledge in these technologies, he has created several step-by-step and easy-to-digest courses. His goal is to help you get ready for the future by learning new technologies and to prepare you to become more productive by getting familiar with the relevant and useful resources. He is still learning and exploring his field of work, and therefore, he welcomes any valuable feedback.
    Note: "Updated in September 2023.". - Online resource; title from title details screen (O'Reilly, viewed October 11, 2023)
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  • 62
    Online Resource
    Online Resource
    [Berkeley] : Apress
    ISBN: 9781484296608 , 1484296605
    Language: English
    Pages: 1 online resource (xvii, 488 pages) , illustrations (chiefly color)
    Edition: Second edition.
    Parallel Title: Erscheint auch als
    Keywords: Python (Computer program language) ; Computer graphics ; Python (Langage de programmation) ; Infographie ; computer graphics
    Abstract: This book shows how to use Python's built-in graphics primitives - points, lines, and arrows - to create complex graphics for the visualization of two- and three-dimensional objects, data sets, and technical illustrations. This updated edition provides more detailed explanations where required, especially regarding Python code, and explores applications to topics of contemporary importance. You'll learn how to create any 2D or 3D object or illustration, as well as how to display images, use color, translate, rotate, shade, add shadows that are cast on other objects, remove hidden lines, plot 2D and 3D data, fit lines and curves to data sets, display points of intersection between 2D and 3D objects, and create digital art. Demonstrations are included which illustrate graphics programming techniques by example, the best way to learn a language. Also brand new to this edition are demonstrations on how to visualize electron probability clouds around a nucleus, climate change, ecological diversity, population dynamics, and resource management. Python source code, including detailed explanations, is included for all applications, making the book more accessible to novice Python programmers. After completing this book, you will be able to create compelling graphic images without being limited to functions available in existing Python libraries. You will: Create 2D and 3D graphic images Add text and symbols to images Shade 3D objects Display cast shadows Use color for maximum effect View 2D and 3D data sets Fit lines and curves to data sets.
    Note: Includes index. - Online resource; title from PDF title page (SpringerLink, viewed December 6, 2023)
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  • 63
    ISBN: 9781484298664 , 1484298667
    Language: English
    Pages: 1 online resource (xxii, 526 pages) , illustrations (some color)
    Edition: Second edition.
    Parallel Title: Erscheint auch als
    Keywords: TensorFlow ; Computer vision ; Neural networks (Computer science) ; OpenCV (Computer program language) ; Application software Development ; Python (Computer program language) ; Image processing ; Machine learning ; Vision par ordinateur ; Réseaux neuronaux (Informatique) ; OpenCV (Langage de programmation) ; Logiciels d'application ; Développement ; Python (Langage de programmation) ; Traitement d'images ; Apprentissage automatique ; image processing
    Abstract: Computer vision is constantly evolving, and this book has been updated to reflect new topics that have emerged in the field since the first edition⁰́₉s publication. All code used in the book has also been fully updated. This second edition features new material covering image manipulation practices, image segmentation, feature extraction, and object identification using real-life scenarios to help reinforce each concept. These topics are essential for building advanced computer vision applications, and you⁰́₉ll gain a thorough understanding of them. The book⁰́₉s source code has been updated from TensorFlow 1.x to 2.x, and includes step-by-step examples using both OpenCV and TensorFlow with Python. Upon completing this book, you⁰́₉ll have the knowledge and skills to build your own computer vision applications using neural networks You will: Understand image processing, manipulation techniques, and feature extraction methods Work with convolutional neural networks (CNN), single-shot detector (SSD), and YOLO Utilize large scale model development and cloud infrastructure deployment Gain an overview of FaceNet neural network architecture and develop a facial recognition system.
    Note: Includes index. - Online resource; title from PDF title page (SpringerLink, viewed November 28, 2023)
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  • 64
    Online Resource
    Online Resource
    [Place of publication not identified] : Manning Publications
    Language: English
    Pages: 1 online resource (1 video file (6 hr., 32 min.)) , sound, color.
    Edition: [First edition].
    DDC: 005.13/3
    Keywords: Python (Computer program language) ; Computer programming ; Python (Langage de programmation) ; Programmation (Informatique) ; computer programming ; Instructional films ; Nonfiction films ; Internet videos ; Films de formation ; Films autres que de fiction ; Vidéos sur Internet
    Abstract: Six Small Algorithm Projects with Python: Video Walkthrough Algorithms are the foundations of computer science. In this series of liveProjects, you'll watch as an expert uses specific algorithms to solve important programming problems. Six Small Algorithm Projects with Python is one of the most popular series on Manning's liveProject platform. Working though the series by hand takes weeks, but now there's a simpler way. In this series of videos, experienced data science lecturer Charles Rice guides you through the entire series, bringing you all the lessons in a fraction of the time. As you watch, you'll learn how to use recursion to draw complex shapes, search linked data structures, and layout and draw trees. You'll learn how to use network algorithms to find shortest paths and assign employees to jobs. As you master the projects in this series, you'll gain an understanding of a variety of algorithmic topics, data structures, and general programming techniques. Rod Stephens, series author Rod Stephens started out as a mathematician but discovered the fun of algorithms and has been programming ever since. Rod was a Microsoft Visual Basic Most Valuable Professional (MVP) for 15 years, has spoken to user groups and conferences, and has taught introductory programming courses. He has written more than 35 books including Essential Algorithms: A Practical Approach to Computer Algorithms Using Python and C# and Beginning Software Engineering, both of which include material related to this series of liveProjects. Charles Rice, video presenter Charles Rice is a Senior Data Science Instructor with the Flatiron School. He has worked in data science education for nearly a decade, teaching machine learning and statistical analysis to hundreds of students in the United States and abroad. In industry, he has worked as a Research Engineer for BlockScience, a blockchain R&D consultancy, and consults on data science and engineering for several FinTech organizations. He is a CompTIA Subject Matter Expert for the forthcoming DataX certification exam. Before getting into code and machine learning, he worked as a journalist and corporate communications professional.
    Note: "Video walkthrough.". - Online resource; title from title details screen (O'Reilly, viewed Decenber 19, 2023)
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  • 65
    ISBN: 9781837631087 , 1837631085 , 9781837632305
    Language: English
    Pages: 1 online resource (438 pages) , illustrations
    Edition: Second edition.
    DDC: 006.3/1
    Keywords: Computational learning theory ; Python (Computer program language) ; Théorie de l'apprentissage informatique ; Python (Langage de programmation)
    Abstract: Computational thinking helps you to develop logical processing and algorithmic thinking while solving real-world problems across a wide range of domains. It's an essential skill that you should possess to keep ahead of the curve in this modern era of information technology. Developers can apply their knowledge of computational thinking to solve problems in multiple areas, including economics, mathematics, and artificial intelligence. This book begins by helping you get to grips with decomposition, pattern recognition, pattern generalization and abstraction, and algorithm design, along with teaching you how to apply these elements practically while designing solutions for challenging problems. You’ll then learn about various techniques involved in problem analysis, logical reasoning, algorithm design, clusters and classification, data analysis, and modeling, and understand how computational thinking elements can be used together with these aspects to design solutions. Toward the end, you will discover how to identify pitfalls in the solution design process and how to choose the right functionalities to create the best possible algorithmic solutions. By the end of this algorithm book, you will have gained the confidence to successfully apply computational thinking techniques to software development.
    Note: Includes index
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  • 66
    Online Resource
    Online Resource
    [Place of publication not identified] : Pearson
    ISBN: 9780138297671 , 0138297673
    Language: English
    Pages: 1 online resource (1 video file (2 hr., 54 min.)) , sound, color.
    Edition: [First edition].
    DDC: 005.13/3
    Keywords: Python (Computer program language) ; Computer programming ; Python (Langage de programmation) ; Programmation (Informatique) ; computer programming ; Instructional films ; Nonfiction films ; Internet videos ; Films de formation ; Films autres que de fiction ; Vidéos sur Internet
    Abstract: 4 Hours of Video Instruction Explore the fundamentals of Python syntax, data types, control flow, and functions while constructing projects. Overview: Python is like a Swiss Army knife for programming. It's a versatile and powerful language that can be used for a wide range of tasks. Just like a Swiss Army knife has various tools that help you in different situations, Python has a toolbox of features and libraries that make it useful for different purposes. It's also widely recognized as the best programming language for beginners due to its wide range of uses and straightforward syntax. Python's versatility and large community support make it an excellent language for beginners to kickstart their programming journey. Skill Up with Python is a beginner course that offers a hands-on approach to learning Python by building a series of practical projects from scratch. Related Learning: Watch and learn from Shaun's other videos: Functional Programming Project with Python 3 (Video Course) Please don't hesitate to get in touch with him about any opportunities or if you'd like to stay up to date on his other courses and live trainings. About the Instructor: Shaun Wassell is a Senior Software Developer who specializes in full-stack development and software architecture. He manages teams of developers, as well as teaches many hundreds of thousands more how to create enterprise-ready applications. Shaun's online courses have over 300,000 learners largely because of his passion for development and his focus on helping people apply their programming skills in the real world. He is a life-long programmer and problem-solving addict whose goal is to help people solve meaningful problems by mastering the art of software development. Skill Level: Beginner What You Will Learn: Practical and Hands-On Learning Experience: Learners are provided with the opportunity to build real-world projects, enabling them to apply their knowledge immediately. Creative and Engaging Approach: By building a variety of projects across different domains, learners are encouraged to think critically, problem-solve, and apply their creativity. Versatility and Broad Skill Development: The project-based Python course covers a range of projects in different areas. This versatility appeals to learners with diverse interests and career aspirations. Who Should Take This Course: Software Developer Data Analyst/Scientist Machine Learning Engineer Web Developer DevOps Engineer Course Requirements: Some basic knowledge of programming, perhaps some web dev or JavaScript experience. About Pearson Video Training: Pearson publishes expert-led video tutorials covering a wide selection of technology topics designed to teach you the skills you need to succeed. These professional and personal technology videos feature world-leading author instructors published by your trusted technology brands: Addison-Wesley, Cisco Press, Pearson IT Certification, Sams, and Que. Topics include IT Certification, Network Security, Cisco Technology, Programming, Web Development, Mobile Development, and more. Learn more about Pearson Video training at http://www.informit.com/video.
    Note: Online resource; title from title details screen (O'Reilly, viewed January 3, 2023)
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  • 67
    ISBN: 9781805120919 , 1805120913
    Language: English
    Pages: 1 online resource
    Edition: 1st edition.
    DDC: 005.13/3
    Keywords: Python (Computer program language) ; Machine learning ; Python (Langage de programmation) ; Apprentissage automatique
    Abstract: "Take your machine learning skills to the next level by mastering the best framework for uncertainty quantification - Conformal Prediction Key Features Master Conformal Prediction, a fast-growing ML framework, with Python applications. Explore cutting-edge methods to measure and manage uncertainty in industry applications. The book will explain how Conformal Prediction differs from traditional machine learning. Book Description In the rapidly evolving landscape of machine learning, the ability to accurately quantify uncertainty is pivotal. ""Practical Guide to Applied Conformal Prediction in Python"" addresses this need by offering an in-depth exploration of Conformal Prediction, a cutting-edge framework set to revolutionize uncertainty management in various ML applications. Embark on a comprehensive journey through Conformal Prediction, exploring its fundamentals and practical applications in binary classification, regression, time series forecasting, imbalanced data, computer vision, and NLP. Each chapter delves into specific aspects, offering hands-on insights and best practices for enhancing prediction reliability. The book concludes with a focus on multi-class classification nuances, providing expert-level proficiency to seamlessly integrate Conformal Prediction into diverse industries. Practical examples in Python using real-world datasets reinforce intuitive explanations, ensuring you acquire a robust understanding of this modern framework for uncertainty quantification. This guide is a beacon for mastering Conformal Prediction in Python, providing a blend of theory and practical application. It serves as a comprehensive toolkit to enhance machine learning skills, catering to professionals from data scientists to ML engineers. What you will learn The fundamental concepts and principles of conformal prediction Learn how conformal prediction differs from traditional ML methods Apply real-world examples to your own industry applications Explore advanced topics - imbalanced data and multi-class CP Dive into the details of the conformal prediction framework Boost your career as a data scientist, ML engineer, or researcher Learn to apply conformal prediction to forecasting and NLP Who this book is for Ideal for readers with a basic understanding of machine learning concepts and Python programming, this book caters to data scientists, ML engineers, academics, and anyone keen on advancing their skills in uncertainty quantification in ML.".
    Note: Includes index. - Modern machine learning approaches. - Description based on online resource; title from digital title page (viewed on January 18, 2024)
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  • 68
    Online Resource
    Online Resource
    [Sebastopol, California] : O'Reilly Media, Inc.
    Language: English
    Pages: 1 online resource (1 video file (55 min.)) , sound, color.
    Edition: [First edition].
    Series Statement: O'Reilly Book Club
    DDC: 005.13/3
    Keywords: Python (Computer program language) ; Object-oriented programming languages ; Programming languages (Electronic computers) ; Instructional films ; Nonfiction films ; Internet videos
    Abstract: Join us for this edition of O'Reilly Book Club with Luciano Ramalho, author of Fluent Python. Learn tricks of the trade, listen to stories, and connect with other fans. What you'll learn and how you can apply it Learn how to write effective, modern Python 3 code by leveraging its best ideas Discover and apply idiomatic Python 3 features beyond your past experience Understand how to make your code shorter, faster, and more readable This recording of a live event is for you because... You want to go beyond the words on the page and hear from the expert. You want to learn from other Pythonistas about next-level Python tactics. Recommended follow-up: Read Fluent Python, second edition (book) Follow and explore Becoming Fluent in Python (expert playlist).
    Note: Online resource; title from title details screen (O’Reilly, viewed February 7, 2023)
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  • 69
    Online Resource
    Online Resource
    [Place of publication not identified] : Packt Publishing
    ISBN: 9781837635092 , 1837635099
    Language: English
    Pages: 1 online resource (1 video file (3 hr., 4 min.)) , sound, color.
    Edition: [First edition].
    DDC: 006.33
    Keywords: Human-computer interaction ; Machine learning ; Python (Computer program language) ; Artificial intelligence ; Artificial intelligence ; Human-computer interaction ; Machine learning ; Python (Computer program language) ; Instructional films ; Internet videos ; Nonfiction films ; Instructional films ; Nonfiction films ; Internet videos
    Abstract: Chatbots are software applications used for online chat conversations through text or text-to-speech instead of providing direct contact with a live human agent. Chatbots are used in dialog systems for various purposes, including customer service, request routing, or information gathering. This course begins with a brief overview of chatbots, their need, and the types of chatbots. We will explore rule-based versus self-learning chatbots. We will understand the working mechanism of chatbots. We will explore machine learning-based chatbots and understand the ML-based architecture of chatbots. You will learn about the purpose of ML-based chatbots and their impact. We will get an overview of the Natural Language Toolkit (NLTK). You will learn to install packages and create a corpus with Python. We will delve into text preprocessing and helper function deployment, generate responses, and implement term-frequency times inverse document-frequency. We will train and test rule-based chatbots and finally work on a project developing an artificial intelligence question-answer chatbot using NLTK. Upon course completion, you will be able to relate the concepts and theories for chatbots in various domains, understand and implement machine learning models for building real-time chatbots, and evaluate machine learning models in chatbots. What You Will Learn Learn about chatbot types, rule-based and self-learning chatbots Learn text preprocessing and develop helper functions with Python Explore the impact and overview of the Natural Language Toolkit Gain hands-on practice, generate text in Python to develop chatbots Explore testing and training of chatbot with machine learning Implement term-frequency times inverse document-frequency hands-on Audience This course delivers content to people wishing to advance their skills in applied machine learning, master data analysis with machine learning, build customized chatbots for their applications, and implement machine learning algorithms for chatbots. This course is for you if you are passionate about rule-based and conversational chatbots. Machine learning practitioners, research scholars, and data scientists can benefit from the course. No prior knowledge of chatbots, or machine learning, is needed. You will need to know basic to intermediate Python coding, which is not taught separately in the course. About The Author AI Sciences: AI Sciences is a group of experts, PhDs, and practitioners of AI, ML, computer science, and statistics. Some of the experts work in big companies such as Amazon, Google, Facebook, Microsoft, KPMG, BCG, and IBM. They have produced a series of courses mainly dedicated to beginners and newcomers on the techniques and methods of machine learning, statistics, artificial intelligence, and data science. Initially, their objective was to help only those who wish to understand these techniques more easily and to be able to start without too much theory. Today, they also publish more complete courses for a wider audience. Their courses have had phenomenal success and have helped more than 100,000 students master AI and data science.
    Note: "Published in February 2023.". - Online resource; title from title details screen (O'Reilly, viewed March 21, 2023)
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  • 70
    Online Resource
    Online Resource
    [Place of publication not identified] : Pragmatic AI Solutions
    Language: English
    Pages: 1 online resource (1 video file (2 hr., 49 min.)) , sound, color.
    Edition: [First edition].
    DDC: 005.1
    Keywords: Python (Computer program language) ; Rust (Computer program language) ; Computer software Development ; Instructional films ; Nonfiction films ; Internet videos
    Abstract: DevOps command-line tools in Python and Rust Use both Python and Rust to automate systems engineering tasks In this introductory course, you will learn how to build powerful command-line tools using Python and Rust. You will discover how to set up a development environment, incorporate user input, expand functionality using modules and libraries, and optimize performance for common DevOps and Systems Engineering tasks. Whether you're new to programming or an experienced developer, this course will teach you the fundamentals of building command-line tools using Python and Rust. By the end of the course, you'll have the skills to automate tasks and improve the efficiency of your DevOps and Systems Engineering workflows. This course includes GitHub repositories that you can use as a reference for all the learning content: Python CLI Examples Rust CLI Examples Learn objectives Set up a development environment for building command-line tools. Build basic command-line tools. Expand the functionality of command-line tools using modules and libraries. Create a simple command-line tool using Rust and understand the basic structure of a Rust-based CLI tool. Incorporate user input, such as arguments and options. Learn how to organize code into modules and packages, and work with dependencies and libraries in both Python and Rust. About your instructor Alfredo Deza has over a decade of experience as a Software Engineer doing DevOps, automation, and scalable system architecture. Before getting into technology he participated in the 2004 Olympic Games and was the first-ever World Champion in High Jump representing Peru. He currently works in Developer Relations at Microsoft and is an Adjunct Professor at Duke University. This solid background in technology and teaching, including his past experience as a Linux system administrator is seen throughout this course, where you will get a first-hand experience with high-level knowledge and practical examples. Resources Pytest Master Class Practical MLOps book.
    Note: Online resource; title from title details screen (O'Reilly, viewed April 24, 2023)
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  • 71
    Online Resource
    Online Resource
    [Place of publication not identified] : Apress
    ISBN: 9781484294543 , 1484294548
    Language: English
    Pages: 1 online resource (1 video file (1 hr., 30 min.)) , sound, color.
    Edition: [First edition].
    DDC: 005.13/3
    Keywords: Python (Computer program language) ; Computer networks Management ; TCP/IP (Computer network protocol) ; Instructional films ; Nonfiction films ; Internet videos
    Abstract: Python offers software engineers a multitude of inter-process communications (IPC) options. With the popularization of cross-cloud communications today, sockets - be they managing TCP, UDP, or even RAW octets - remain the predominate way to monitor, aggregate, as well as to participate in point-to-point, as well as multi-process signaling and information sharing activities. The purpose of the PSL-3000 Socket IPC is to review how to use TCP/IP and UDP/IP - the most popular and ubiquitous of all modern communication patterns and protocols. Along the way not only will PSL-3000 Socket IPC demonstrate how to share data between cooperating computer processes using IP, but many supporting options - as well as Python's built-in, object-oriented socket frameworks. From protocols, options, and failure recovery, along the way hands-on "beyond the basics" discussions, demonstrations, and coding activities are provided. Evolutionary IPC concepts destined to help viewers demystify common networking terms, trends, technologies, as well as techniques. Split into three (3) major sections, the PSL-3000 Socket IPC educational opportunity viewers will enjoy many IPC coding activities. Projects encompass short and simple snippets designed to review basic networking concepts, common design patterns, bit manipulations, and even hexadecimal network-mask encodings. - Viewers will also enjoy completing an evolutionary set of IPC activities covering client, server, peer-to-peer (multicasting,) as well as Python 3's "batteries included" socketed Framework. Timeouts, testing, as well as error recovery strategies are also demonstrated & practiced. What you will learn: Understand common communications terms, technologies, as well as testing techniques. How to use advanced techniques to multicast. What are the advantage of Python's socketserver object oriented design. How to simulate, as well as to recover from, common communication errors. Who this book is for: Both beginner and intermediate 'Pythoneers understanding functional Python, as well as the basics of inheriting from Python classes, will guarantee a larger audience. DevOps, Cloud, and IT security roles will also enjoy discovering how quick & easy it can be to create, monitor as well as to share data, events, and operational telemetries using Python 3.
    Note: Online resource; title from title details screen (O'Reilly, viewed May 11, 2023)
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  • 72
    Online Resource
    Online Resource
    [Place of publication not identified] : Packt Publishing
    ISBN: 9781805127956 , 1805127950
    Language: English
    Pages: 1 online resource (1 video file (4 hr., 1 min.)) , sound, color.
    Edition: [First edition].
    DDC: 005.1/17
    Keywords: Object-oriented programming (Computer science) ; Python (Computer program language) ; Instructional films ; Nonfiction films ; Internet videos
    Abstract: Object-Oriented Programming (OOP) is a crucial paradigm in modern programming that allows developers to write efficient, modular, and scalable code. It helps in organizing code, creating reusable and maintainable code, and improving code readability. In this course, we will dive deep into OOP concepts and provide you with a solid understanding of the fundamentals, advanced topics, and real-world applications of OOP in Python. This course covers a wide range of topics, starting with the basics of OOP, including classes, objects, attributes, methods, and constructors. You will learn about encapsulation, abstraction, inheritance, and polymorphism, which are the building blocks of OOP. You will also explore advanced concepts such as class methods, static methods, operator overloading, and dunder methods, along with practical examples and exercises to solidify your understanding. To apply the concepts learned, you will build a real-world project, the Library Management System, where you will learn to create classes, objects, and methods for managing books and users. You will also gain hands-on experience with method overriding, multiple inheritances, and other advanced topics while building practical applications. By the end of this course, you will have a deep understanding of OOP concepts in Python, be able to build robust and scalable applications using OOP principles, and possess the skills to write clean, efficient, and maintainable Python code. What You Will Learn Understand the fundamentals of Object-Oriented Programming in Python Build real-world projects using OOP concepts in Python Apply polymorphism in object-oriented styles using multiple strategies Implement OOP principles to create reusable and maintainable Python code Build a Library Management System using OOP concepts Master the use of classes, objects, attributes, methods, and constructors in Python Audience This course is tailored for programmers and developers who aspire to deepen their understanding of Object-Oriented Programming (OOP) concepts and apply them in real-world applications. It is well-suited for beginners who are already familiar with Python basics and are looking to transition into OOP programming. Intermediate Python developers seeking to enhance their coding skills and learn advanced OOP topics will also find value in this course. Prior knowledge of Python programming concepts is required, including variables, data types, loops, and functions. If you are eager to learn and apply OOP principles practically in Python, this comprehensive course will provide you with the knowledge and skills needed to succeed. About The Author Meta Brains: Meta Brains is a professional training brand developed by a team of software developers and finance professionals who have a passion for finance, coding, and Excel. They bring together both professional and educational experiences to create world-class training programs accessible to everyone. Currently, they are focused on the next great revolution in computing: the Metaverse. Their ultimate objective is to train the next generation of talent so that we can code and build the metaverse together!.
    Note: Online resource; title from title details screen (O'Reilly, viewed May 23, 2023)
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  • 73
    Language: German
    Pages: 1 online resource (432 pages) , illustrations
    Edition: 1. Auflage.
    Uniform Title: Natural language processing with transformers
    DDC: 006.3/5
    Keywords: Natural language processing (Computer science) ; Python (Computer program language) ; Machine learning ; Cloud computing
    Abstract: Transformer haben sich seit ihrer Einführung nahezu über Nacht zur vorherrschenden Architektur im Natural Language Processing entwickelt. Sie liefern die besten Ergebnisse für eine Vielzahl von Aufgaben bei der maschinellen Sprachverarbeitung. Wenn Sie Data Scientist oder Programmierer sind, zeigt Ihnen dieses praktische Buch, wie Sie NLP-Modelle mit Hugging Face Transformers, einer Python-basierten Deep-Learning-Bibliothek, trainieren und skalieren können. Transformer kommen beispielsweise beim maschinellen Schreiben von Nachrichtenartikeln zum Einsatz, bei der Verbesserung von Google-Suchanfragen oder bei Chatbots. In diesem Handbuch zeigen Ihnen Lewis Tunstall, Leandro von Werra und Thomas Wolf, die auch die Transformers-Bibliothek von Hugging Face mitentwickelt haben, anhand eines praktischen Ansatzes, wie Transformer-basierte Modelle funktionieren und wie Sie sie in Ihre Anwendungen integrieren können. Sie werden schnell eine Vielzahl von Aufgaben wie Textklassifikation, Named Entity Recognition oder Question Answering kennenlernen, die Sie mit ihnen lösen können.
    Note: Includes bibliographical references and index
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  • 74
    Online Resource
    Online Resource
    [Germany] : mitp Verlag
    ISBN: 9783747506424 , 3747506429
    Language: German
    Pages: 1 online resource (208 pages) , illustrations
    Edition: 2. Auflage.
    DDC: 005.13/3
    Keywords: Raspberry Pi (Computer) ; Python (Computer program language) ; Application software Development ; Microcontrollers
    Abstract: Dieses Buch ist ein kompakter und praktischer Leitfaden für den Raspberry Pi Pico und Pico W inklusive der Programmierung mit MicroPython. Sie lernen zunächst die Hardware mit allen Anschlüssen und technischen Daten sowie die Firmware kennen und erfahren, wie Sie die Entwicklungsumgebung Thonny installieren und konfigurieren. Anschließend behandelt der Autor alle Themen, die für den ersten Einstieg relevant sind: Ein- und Ausgänge, Verarbeitung analoger Daten, Temperaturmesser, digitale Anzeigen wie LED, LCD und OLED sowie die Verwendung von Schnittstellen wie UART, I2C und Wifi. Zum Abschluss zeigt Ihnen der Autor weitere mögliche Programmerweiterungen. Mit diesem Buch sind Sie bestens vorbereitet, den Raspberry Pi Pico selbstständig für eigene Projekte einzusetzen.
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  • 75
    Online Resource
    Online Resource
    [Place of publication not identified] : Manning Publications
    Language: English
    Pages: 1 online resource (1 video file (17 hr., 3 min.)) , sound, color.
    Edition: [First edition].
    DDC: 005.133
    Keywords: Python (Computer program language) ; Hacking ; Computer security ; Penetration testing (Computer security) ; Computer programmers Professional ethics ; Instructional films ; Nonfiction films ; Internet videos
    Abstract: Master ethical hacking while working on practical Python coding projects. Learn Python hacking skills, ethical hacking principles, penetration testing, cybersecurity, and more with over 17 hours of video tutorials. Designed for those with intermediate- to advanced-level understanding of ethical hacking, this course is still taught in a step-by-step, beginner-friendly method. We begin with coding basic port and vulnerability scanning tools; move onto SSH, FTP, and spoofing attacks; take a look at network analysis; and finish up with coding a reverse shell, command and control center, and several website penetration testing tools. Upon completing this course, you will be prepared for a job in the cybersecurity industry. Learn all these skills while completing eight coding projects: Port and vulnerability scanner SSH brute-forcer MAC address changer ARP spoofer DNS spoofer Multi-functioning reverse shell Keylogger Command and control center.
    Note: Online resource; title from title details screen (O'Reilly, viewed May 23, 2023)
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  • 76
    Online Resource
    Online Resource
    [Place of publication not identified] : Packt Publishing
    ISBN: 9781803237466 , 1803237465
    Language: English
    Pages: 1 online resource (1 video file (2 hr., 12 min.)) , sound, color.
    Edition: [First edition].
    DDC: 005.13/3
    Keywords: Facebook (Electronic resource) ; Python (Computer program language) ; Time-series analysis Data processing ; Machine learning ; R (Computer program language) ; Instructional films ; Nonfiction films ; Internet videos
    Abstract: Prophet enables Python and R developers to build scalable time series forecasts. This course will help you implement Prophet's cutting-edge forecasting techniques to model future data with higher accuracy and with very few lines of code. In this course, you will learn how to use Facebook Prophet to do time series analysis and forecasting. You will learn how the Prophet works under the hood (that is, what are its modeling assumptions?) and the Prophet API (that is, how to write the code). This course is a practice-oriented course, demonstrating how to prepare your data for Prophet, fit a model, and use it to forecast, analyze the results, and evaluate the model's predictions. We will apply Prophet to a variety of datasets, including store sales and stock prices. You will learn how to use Prophet to plot the model's in-sample predictions and forecast. Then, learn how to plot the components of the fitted model. You will also learn how to deal with outliers, missing data, and non-daily (for example, monthly) data. By the end of this course, you will be able to use Prophet confidently to forecast your data. What You Will Learn Prepare your data (a Pandas dataframe) for Facebook Prophet Learn how to fit a Prophet model to a time series Plot the components of the fitted model Model holidays and exogenous regressors Evaluate your model with forecasting metrics Learn how to do changepoint detection with Prophet Audience Anyone interested in data science, machine learning, or who wishes to use time series analysis on their own data should take this course. Good Python programming skills are required, as well as knowledge of Pandas, Dataframes, and preferably some familiarity with Scikit-Learn, though this is not required. About The Author Lazy Programmer: The Lazy Programmer is an AI and machine learning engineer with a focus on deep learning, who also has experience in data science, big data engineering, and full-stack software engineering. With a background in computer engineering and specialization in machine learning, he holds two master's degrees in computer engineering and statistics with applications to financial engineering. His expertise in online advertising and digital media includes work as both a data scientist and big data engineer. He has created deep learning models for prediction and has experience in recommendation systems using reinforcement learning and collaborative filtering. He is a skilled instructor who has taught at universities including Columbia, NYU, Hunter College, and The New School. He has web programming expertise, with experience in technologies such as Python, Ruby/Rails, PHP, and Angular, and has provided his services to multiple businesses.
    Note: Online resource; title from title details screen (O'Reilly, viewed March 20, 2023)
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  • 77
    ISBN: 9788383227528 , 8383227523
    Language: Polish
    Pages: 1 online resource (320 pages) , illustrations
    Edition: [First edition].
    Uniform Title: Introduction to machine learning with Python
    DDC: 005.13/3
    Keywords: Machine learning ; Python (Computer program language) ; Data mining
    Abstract: Uczenie maszynowe kojarzy się z dużymi firmami i rozbudowanymi zespołami. Prawda jest taka, że obecnie można samodzielnie budować zaawansowane rozwiązania uczenia maszynowego i korzystać do woli z olbrzymich zasobów dostępnych danych. Trzeba tylko mieć pomysł i... trochę podstawowej wiedzy. Tymczasem większość opracowań na temat uczenia maszynowego i sztucznej inteligencji wymaga biegłości w zaawansowanej matematyce. Utrudnia to naukę tego zagadnienia, mimo że uczenie maszynowe jest coraz powszechniej stosowane w projektach badawczych i komercyjnych. Ta praktyczna książka ułatwi Ci rozpoczęcie wdrażania rozwiązań rzeczywistych problemów związanych z uczeniem maszynowym. Zawiera przystępne wprowadzenie do uczenia maszynowego i sztucznej inteligencji, a także sposoby wykorzystania Pythona i biblioteki scikit-learn, uwzględniające potrzeby badaczy i analityków danych oraz inżynierów pracujących nad aplikacjami komercyjnymi. Zagadnienia matematyczne ograniczono tu do niezbędnego minimum, zamiast tego skoncentrowano się na praktycznych aspektach algorytmów uczenia maszynowego. Dokładnie opisano, jak konkretnie można skorzystać z szerokiej gamy modeli zaimplementowanych w dostępnych bibliotekach.
    Note: Includes index
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  • 78
    ISBN: 9783747506721 , 3747506720
    Language: German
    Pages: 1 online resource (304 pages) , illustrations
    Edition: 4. Auflage.
    Series Statement: Let's play
    DDC: 794.8
    Keywords: Minecraft (Game) Handbooks, manuals, etc ; Video games Handbooks, manuals, etc ; Adventure video games Handbooks, manuals, etc ; Python (Computer program language) ; Minecraft (Game) ; Python (Computer program language) ; Video games ; Handbooks and manuals
    Abstract: Du spielst schon lange Minecraft und denkst, du hast schon alles gesehen? Kennst du schon das Feuerschwert, den Enderbogen oder den Spielmodus »Schneeballschlacht«? Du willst auf Knopfdruck Türme, Mauern oder sogar ganze Häuser bauen? Vollautomatisch auf Geschehnisse in der Spielwelt reagieren? Mit eigenen Plugins kannst du all das und noch viel mehr entdecken und ganz nebenbei auch noch programmieren lernen. Python ist für Programmiereinsteiger besonders leicht zu lernen. Daniel Braun zeigt dir, wie du mit Python und Bukkit oder Spigot Erweiterungen für Minecraft programmierst, sogenannte Plugins, die du dann zusammen mit deinen Freunden auf deinem eigenen Minecraft-Server ausprobieren kannst. Dafür sind keine Vorkenntnisse erforderlich, du lernst alles von Anfang an. Nach dem Programmieren einfacher Chat-Befehle wirst du coole Plugins zum Bauen erstellen, so dass mit einem einzigen Befehl sofort z.B. ein fertiges Haus oder eine Kugel vor dir steht. Außerdem erfährst du, wie deine Plugins automatisch auf Geschehnisse in der Spielwelt reagieren können. Du kannst auch eigene Crafting-Rezepte entwerfen, um z.B. mächtige neue Waffen zu kreieren wie das Feuerschwert, das alles in Brand setzt, worauf es trifft. Am Ende lernst du sogar, wie du eigene Spielmodi entwickeln kannst, also ein Spiel im Spiel. Ob eine Schneeballschlacht mit Highscore-Liste oder ein Wettsammeln mit Belohnung für den Sieger, hier ist jede Menge Spaß garantiert. Für das alles brauchst du keine Vorkenntnisse, nur Spaß am Programmieren. Es beginnt mit ganz einfachen Beispielen, aber mit jedem Kapitel lernst du mehr Möglichkeiten kennen, um Minecraft nach deinen Wünschen anzupassen. Am Ende kannst du richtig in Python programmieren und deiner Kreativität sind keine Grenzen mehr gesetzt, um deine eigene Minecraft-Welt zu erschaffen.
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  • 79
    Online Resource
    Online Resource
    Shelter Island, NY : Manning Publications
    ISBN: 9781617297939 , 1617297933
    Language: English
    Pages: 1 online resource (xviii, 283 pages) , illustrations
    Parallel Title: Erscheint auch als Rodrigues Antão, Tiago Fast Python
    DDC: 005.13/3
    Keywords: Python (Computer program language) ; Electronic data processing ; Python (Langage de programmation) ; Electronic data processing ; Python (Computer program language)
    Abstract: Fast Python is your guide to optimizing every part of your Python-based data analysis process, from the pure Python code you write to managing the resources of modern hardware and GPUs. You'll learn to rewrite inefficient data structures, improve underperforming code with multithreading, and simplify your datasets without sacrificing accuracy. Written for experienced practitioners, this book dives right into practical solutions for improving computation and storage efficiency. You'll experiment with fun and interesting examples such as rewriting games in Cython and implementing a MapReduce framework from scratch. Finally, you'll go deep into Python GPU computing and learn how modern hardware has rehabilitated some former antipatterns and made counterintuitive ideas the most efficient way of working.
    Note: Includes index
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  • 80
    Online Resource
    Online Resource
    [Place of publication not identified] : Packt Publishing
    ISBN: 9781835082133 , 1835082130
    Language: English
    Pages: 1 online resource (1 video file (2 hr., 40 min.)) , sound, color.
    Edition: [First edition].
    DDC: 005.4/37
    Keywords: Graphical user interfaces (Computer systems) Programming ; Python (Computer program language) ; Object-oriented programming (Computer science) ; Instructional films ; Nonfiction films ; Internet videos
    Abstract: Tkinter is a powerful and widely-used GUI toolkit for Python. It allows developers to create desktop apps with interactive user interfaces. With Tkinter, developers can design windows, buttons, menus, text boxes, and other widgets, making it an essential tool for building visually appealing and user-friendly applications. Python's ease of use, combined with Tkinter's versatility, make it an excellent choice for GUI development, enabling programmers to develop desktop apps in Python The course content includes an introductory section on GUI programming and Tkinter. It then proceeds to establish a strong foundation by exploring basic Tkinter widgets. As the course advances, it delves into more advanced widgets and layouts. Students will learn to master event handling to create interactive interfaces. The course covers the design and structure of robust GUI applications, customization of widget and styling, and best practices for writing efficient code. Real-world projects provide practical applications of the learned knowledge. Upon completion, we will be proficient in GUI programming with Tkinter, possess knowledge of basic and advanced widgets, understand event handling, be skilled in designing robust applications, customizing widgets, and implementing best coding practices, and have hands-on experience with real-world projects. What You Will Learn Learn the fundamentals of GUI development and the Tkinter library Create visually appealing interfaces with Tkinter widgets Master layout management for responsive applications Explore advanced Tkinter widgets and techniques Handle user interactions and employ event-driven programming Customize widget appearance and write efficient code for practical projects Audience This course is for Python developers aiming to venture into GUI development, building desktop apps with Python and Tkinter, computer science or software engineering students, and anyone keen to create useful and interactive desktop applications. Individuals looking to enhance their portfolio with user-friendly interfaces and those enthusiastic about exploring Python's GUI programming can benefit. Prerequisites include an eagerness to learn and experiment with GUI development using Tkinter, basic math skills, and a passion for learning. Prior experience in any programming language is desirable. About The Author Meta Brains: Meta Brains is a team of passionate software developers and finance professionals. They provide professional training programs that combine their expertise in coding, finance, and Excel. With a focus on the Metaverse, they aim to equip learners with the necessary skills to participate in the next computing revolution. Their inclusive approach ensures accessibility to everyone, fostering a community that collaboratively codes and builds the future of the Metaverse.
    Note: "Published in August 2023.". - Online resource; title from title details screen (O'Reilly, viewed August 14, 2023)
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  • 81
    Online Resource
    Online Resource
    [Place of publication not identified] : Pragmatic AI Solutions
    Language: English
    Pages: 1 online resource (1 video file (3 hr., 27 min.)) , sound, color.
    Edition: [First edition].
    DDC: 005.13/3
    Keywords: Python (Computer program language) ; SQL (Computer program language) ; Computer programming ; Instructional films ; Nonfiction films ; Internet videos
    Abstract: Scripting with Python and SQL for Data Engineering Learn Python data structures, web scraping, SQL, and MySQL from the ground up. Master essential skills for collecting, storing, and working with data. In this hands-on course for beginners, you'll learn how to: Store and manipulate data using Python lists, dictionaries, JSON Write reusable scripts to process data Connect Python to databases like SQLite and MySQL Query, import, and export data with SQL Scrape and parse websites using Beautiful Soup and Scrapy Persist scraped data to databases and files You'll use the following example repositories to practice: Mapping data in Python Python Scripting Scrapping Basics Key topics include: Mapping, iterating, and persisting data structures Creating modules, scripts, and workflows in Python SQL essentials - queries, statements, aggregations Setting up connections from Python to SQLite and MySQL Scraping data locally and at scale with spiders Storing scraped data to optimize pipelines You'll build your data wrangling skills through practical examples and hands-on coding exercises in every lesson. By the end of the course, you'll have experience building end-to-end data engineering scripts. Whether you're a beginner looking to learn Python and SQL, or want to develop robust data engineering skills, this course will get you started. Enroll now and start collecting, storing, and working with data using Python and SQL You'll gain hands-on experience building Python scripts and SQL queries for common data engineering tasks. This course is divided in 4 weeks: Week 1 Working with Data in Python By the end of Week 1 you'll be able to: Apply Python data structures like lists, dicts Extract data from sources like CSV, JSON Load and persist data using JSON Lesson 1: Data Structures in Python Lesson Outline Lists, tuples, dictionaries Working with pandas DataFrames Loading data files like CSV into data structures Lesson 2: Reading and Writing Data Lesson Outline Reading and writing CSV files Serializing Python objects with JSON Parsing and dumping JSON data Lesson 3: Persisting and Loading Data in Python Lesson Outline Loading data from files Saving data from Python to disk Loading and saving data to JSON Week 2 Python Scripting and SQL By the end of Week 2 you'll be able to: Write reusable Python scripts Use SQLite to persist data Query SQLite databases with Python Lesson 1: Python Scripting Techniques Lesson Outline Writing modular, reusable Python scripts Exception handling and logging Python virtual environments Lesson 2: Python with SQLite Lesson Outline Creating SQLite databases from Python Writing tables with SQLAlchemy Querying SQLite from Python with SQLAlchemy Week 3 Learning Objectives By the end of Week 3 you'll be able to: Scrape and collect data from websites Build scalable scraping scripts Persist scraped data to files/databases Lesson 1: Web Scraping with Python Lesson Outline HTML parsing and structure Using Beautiful Soup for scraping Storing scraped data in Python Lesson 2: Scalable Web Scraping Lesson Outline Scraping best practices Scaling scraping with multiprocessing Storing scraped data in databases Week 4 Learning Objectives By the end of Week 4 you'll be able to: Connect to MySQL from Python Execute SQL statements and queries Import and export data from MySQL Lesson 1: Python and MySQL Lesson Outline Installing MySQL and configuration Connecting Python to MySQL Executing queries and statements Lesson 2: Running SQL queries from VSCode Use Visual Studio Code to build SQL queries Execute and review SQL queries from Visual Studio Code Lesson 3: Importing and Exporting Data Lesson Outline Loading and exporting CSV data Best practices for moving data into MySQL Automating data imports with Python About your instructor Alfredo Deza has over a decade of experience as a Software Engineer doing DevOps, automation, and scalable system architecture. Before getting into technology he participated in the 2004 Olympic Games and was the first-ever World Champion in High Jump representing Peru. He currently works in Developer Relations at Microsoft and is an Adjunct Professor at Duke University. This solid background in technology and teaching, including his experience teaching and authoring content about DevOps and MLOps will give you everything you need to get started applying these powerful concepts. Resources Python and Rust CLI Tools Linux For Beginners Hands on Python for MLOps.
    Note: Online resource; title from title details screen (O'Reilly, viewed August 14, 2023)
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  • 82
    Online Resource
    Online Resource
    [Place of publication not identified] : Pragmatic AI Solutions
    Language: English
    Pages: 1 online resource (1 video file (1 hr., 32 min.)) , sound, color.
    Edition: [First edition].
    Series Statement: Rough draft
    DDC: 005.13/3
    Keywords: Rust (Computer program language) ; Python (Computer program language) ; Instructional films ; Nonfiction films ; Internet videos
    Abstract: Using Rust with Python A Practical Guide to Rust for Python Developers Topics: Lesson 1: Introduction to Rust and Python Integration & PyO3 Basics Video 1.1 : Introduction to Rust and Python Video 1.2 : PyO3 Installation Video 1.3 : Basic Rust Library Video 1.4 : Rust to Python Video 1.5 : Rust Ownership Model Video 1.6 : Diagram PyO3 Project Video 1.7 : Python Calc CLI Video 1.8 : PyO3 Features Video 1.9 : PyO3 Exceptions Lesson 2: Advanced Rust and Python Mixing Techniques Video 1.10 : Call Python from Rust Video 1.11 : Run Python Embedded in Rust Video 1.12 : Embedded Rust CLI Diagram Video 1.13 : Embedded Rust CLI Video 1.14 : Embedded Rust CLI Test Video 1.15 : Rust-built Python Tools Video 1.16 : Using Rust Ruff Linter Lesson 3: Python and Rust Command-Line Tools & Recap Video 1.17 : Using Polars with Python and Rust Video 1.18 : Polars CLI in Rust Video 1.19 : Polars CLI Test in Rust Video 1.20 : Polars-CLI Integration Test Video 1.21 : Polars Criterion Benchmarking Learning Objectives Understand the need for Rust and Python integration. Explore the PyO3 library and its usage. Understand the Rust ownership model and its usage in PyO3. Who Should Take This Course Python developers who want to learn Rust and its integration with Python. Rust developers who want to learn how to integrate Rust with Python. Developers who want to learn how to build command-line tools using Rust and Python.
    Note: Online resource; title from title details screen (O'Reilly, viewed September 06, 2023)
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  • 83
    Online Resource
    Online Resource
    [Erscheinungsort nicht ermittelbar] : Packt Publishing
    ISBN: 9781801817356 , 1801817359
    Language: Undetermined
    Pages: 1 online resource (1 video file)
    DDC: 006.3/2
    Keywords: Neural networks (Computer science) ; Machine learning ; Python (Computer program language) ; Réseaux neuronaux (Informatique) ; Apprentissage automatique ; Python (Langage de programmation) ; Instructional films ; Nonfiction films ; Internet videos ; Films de formation ; Films autres que de fiction ; Vidéos sur Internet
    Abstract: Image segmentation is a key technology in the field of computer vision, which enables computers to understand the content of an image at a pixel level. It has numerous applications, including autonomous vehicles, medical imaging, and augmented reality. You will start by exploring tensor handling, automatic gradient calculation with autograd, and the fundamentals of PyTorch model training. As you progress, you will build a strong foundation, covering critical topics such as working with datasets, optimizing hyperparameters, and the art of saving and deploying your models. With a robust understanding of PyTorch, you will dive into the heart of the course--semantic segmentation. You will explore the architecture of popular models such as UNet and FPN, understand the intricacies of upsampling, grasp the nuances of various loss functions, and become fluent in essential evaluation metrics. Moreover, you will apply this knowledge in real-world scenarios, learning how to train a semantic segmentation model on a custom dataset. This practical experience ensures that you are not just learning theory but gaining the skills to tackle actual projects with confidence. By course end, you will wield the power to perform multi-class semantic segmentation on real-world datasets. What You Will Learn Implement multi-class semantic segmentation with PyTorch Explore UNet and FPN architectures for image segmentation Understand upsampling techniques and their importance in deep learning Learn the theory behind loss functions and evaluation metrics Perform efficient data preparation to reshape inputs to the appropriate format Create a custom dataset class for image segmentation in PyTorch Audience This course is tailored to a diverse audience, making it accessible to both newcomers and experienced individuals in the field of computer vision. If you are an aspiring developer eager to delve into image segmentation or a data scientist aiming to expand your deep learning repertoire, this course is for you. While no prior image segmentation knowledge is required, a fundamental understanding of Python is essential. Familiarity with machine learning concepts will be beneficial. About The Author Bert Gollnick: Bert Gollnick is a proficient data scientist with substantial domain knowledge in renewable energies, particularly wind energy. With a rich background in aeronautics and economics, Bert brings a unique perspective to the field. Currently, Bert holds a significant role at a leading wind turbine manufacturer, leveraging his expertise to contribute to innovative solutions. For several years, Bert has been a dedicated instructor, offering comprehensive training in data science and machine learning using R and Python. The core interests of Bert lie at the crossroads of machine learning and data science, reflecting a commitment to advancing these disciplines.
    Note: Machine-generated record
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  • 84
    Online Resource
    Online Resource
    [Erscheinungsort nicht ermittelbar] : Packt Publishing
    ISBN: 9781835465011 , 1835465013
    Language: Undetermined
    Pages: 1 online resource (1 video file)
    DDC: 005.1/4
    Keywords: Python (Computer program language) ; Computer software Testing ; Debugging in computer science ; Python (Langage de programmation) ; Débogage ; Instructional films ; Nonfiction films ; Internet videos ; Films de formation ; Films autres que de fiction ; Vidéos sur Internet
    Abstract: Playwright is a cutting-edge browser automation tool that empowers you to test and automate seamlessly. Pair this with Pytest, a versatile Python testing framework, and you will unlock a whole new level of testing prowess. In this course, you go through the integration of Playwright's capabilities into the Pytest framework, leveraging the power of the pytest-playwright plugin. Starting with the fundamentals of testing using Pytest, dive into the pytest-playwright plugin and explore a range of Playwright tools. Learn to use web-first assertions with expect(), conquer modern testing challenges in the UI Testing Playground, and gain mastery over Playwright fixtures, custom setup/teardown routines, and test hooks. Implement the Page Object Model for organized testing, handle network events seamlessly, and delve into REST API testing, authentication, and API request context. Next, you will discover optimization techniques such as parallelism, and understand how to deploy your automated tests seamlessly into your CI pipeline. Parameterize your tests with predefined test data for versatility and efficiency, and embrace the principles of behavior-driven development with Python Behave. By the course's end, you will have the skills and knowledge to craft automated tests that deliver speed, reliability, and robustness using Playwright and Pytest. What You Will Learn Write Python scripts to launch browsers and automate tasks performed Locate web elements using various methods and perform actions on web elements Log in to websites and authenticate yourself using your accounts Write a script to automate inbox mail checking for new emails and reporting Deploy automated tests using GitHub CI Practice behavior-driven development with Playwright and Python Behave Audience This course caters to a diverse audience, primarily targeting beginner Python developers who are keen to explore the world of web automation testing. If you are just starting your journey in Python and aspire to master web automation, this course offers a solid foundation. Additionally, automation testers looking to broaden their skill set, specifically in Playwright, will find this course invaluable. No prior knowledge is necessary, making it an accessible starting point for anyone interested in web automation and testing. A basic understanding of Python and HTML is recommended. About The Author Rahul Mula: Rahul Mula is a passionate developer with expertise in Python, Flutter, and web development. He was really intrigued the first time he learned about programming and realized what could be done with it. Rahul thrives on exploring diverse technologies and crafting innovative applications. He's the mastermind behind Keyviz, a remarkable open-source tool for real-time keystroke visualization. Rahul's contributions extend to the realm of education, where he has authored books and crafted courses on Python programming, benefiting thousands of eager learners.
    Note: Machine-generated record
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  • 85
    Online Resource
    Online Resource
    [Place of publication not identified] : Packt Publishing
    ISBN: 9781801070089 , 1801070083
    Language: English
    Pages: 1 online resource (1 video file (17 hr., 39 min.)) , sound, color.
    Edition: [First edition].
    DDC: 006.3/2
    Keywords: Neural networks (Computer science) ; Machine learning ; Python (Computer program language) ; Instructional films ; Nonfiction films ; Internet videos
    Abstract: PyTorch is a Python framework developed by Facebook to develop and deploy deep learning models. It is one of the most popular deep-learning frameworks nowadays. You will begin with learning the deep learning concept. Dive deeper into tensor handling, acquiring the finesse to create and manipulate tensors while leveraging PyTorch's automatic gradient calculation through Autograd. Then transition to modeling by constructing linear regression models from scratch. After that, you will dive deep into classification models, mastering both multilabel and multiclass. You will then see the theory behind object detection and acquire the prowess to build object detection models. Embrace the cutting edge with YOLO v7, YOLO v8, and faster RCNN, and unleash the potential of pre-trained models and transfer learning. Delve into RNNs and look at recommender systems, unlocking matrix factorization techniques to provide personalized recommendations. Refine your skills in model debugging and deployment, where you will debug models using hooks, and navigate the strategies for both on-premise and cloud deployment. Finally, you will explore ChatGPT, ResNet, and Extreme Learning Machines. By the end of this course, you will have learned the key concepts, models, and techniques, and have the confidence to craft and deploy robust deep-learning solutions. What You Will Learn Grasp deep learning concepts and install tools/packages/IDE/libraries Master CNN theory, image classification, layer dimensions, and transformations Dive into audio classification using torchaudio and spectrograms Do object detection with the help of YOLO v7, YOLO v8, and Faster RCNN Learn word embeddings, sentiment analysis, and pre-trained NLP models Deploy models using Google Cloud and other strategies Audience This course is ideal for Python developers and data enthusiasts seeking to expand their skills. This will also benefit aspiring data scientists, machine learning engineers, AI enthusiasts, and anyone intrigued by the transformative potential of deep learning. Whether you are a beginner or possess some prior knowledge, this course offers a smooth progression that will empower you to develop, deploy, and innovate with deep learning models using PyTorch. Basic Python knowledge is required to fully engage with the material. About The Author Bert Gollnick: Bert Gollnick is a proficient data scientist with substantial domain knowledge in renewable energies, particularly wind energy. With a rich background in aeronautics and economics, Bert brings a unique perspective to the field. Currently, Bert holds a significant role at a leading wind turbine manufacturer, leveraging his expertise to contribute to innovative solutions. For several years, Bert has been a dedicated instructor, offering comprehensive training in data science and machine learning using R and Python. The core interests of Bert lie at the crossroads of machine learning and data science, reflecting a commitment to advancing these disciplines.
    Note: "Updated September 2023.". - Online resource; title from title details screen (O'Reilly, viewed October 10, 2023)
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  • 86
    ISBN: 9781484296066 , 1484296060
    Language: English
    Pages: 1 online resource
    Parallel Title: Erscheint auch als
    Keywords: Reinforcement learning ; Feedback control systems ; Python (Computer program language) ; Apprentissage par renforcement (Intelligence artificielle) ; Systèmes à réaction ; Python (Langage de programmation)
    Abstract: Unlock the full potential of reinforcement learning (RL), a crucial subfield of Artificial Intelligence, with this comprehensive guide. This book provides a deep dive into RL's core concepts, mathematics, and practical algorithms, helping you to develop a thorough understanding of this cutting-edge technology. Beginning with an overview of fundamental concepts such as Markov decision processes, dynamic programming, Monte Carlo methods, and temporal difference learning, this book uses clear and concise examples to explain the basics of RL theory. The following section covers value function approximation, a critical technique in RL, and explores various policy approximations such as policy gradient methods and advanced algorithms like Proximal Policy Optimization (PPO). This book also delves into advanced topics, including distributed reinforcement learning, curiosity-driven exploration, and the famous AlphaZero algorithm, providing readers with a detailed account of these cutting-edge techniques. With a focus on explaining algorithms and the intuition behind them, The Art of Reinforcement Learning includes practical source code examples that you can use to implement RL algorithms. Upon completing this book, you will have a deep understanding of the concepts, mathematics, and algorithms behind reinforcement learning, making it an essential resource for AI practitioners, researchers, and students. What You Will Learn Grasp fundamental concepts and distinguishing features of reinforcement learning, including how it differs from other AI and non-interactive machine learning approaches Model problems as Markov decision processes, and how to evaluate and optimize policies using dynamic programming, Monte Carlo methods, and temporal difference learning Utilize techniques for approximating value functions and policies, including linear and nonlinear value function approximation and policy gradient methods Understand the architecture and advantages of distributed reinforcement learning Master the concept of curiosity-driven exploration and how it can be leveraged to improve reinforcement learning agents Explore the AlphaZero algorithm and how it was able to beat professional Go players Who This Book Is For Machine learning engineers, data scientists, software engineers, and developers who want to incorporate reinforcement learning algorithms into their projects and applications.
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  • 87
    Online Resource
    Online Resource
    [Place of publication not identified] : O'Reilly Media, Inc.
    Language: English
    Pages: 1 online resource (1 video file (4 hr., 21 min.)) , sound, color.
    Edition: [First edition].
    DDC: 005.13/3
    Keywords: Mathematical optimization ; Search engines ; Python (Computer program language) ; Machine learning ; Optimisation mathématique ; Moteurs de recherche ; Python (Langage de programmation) ; Apprentissage automatique ; search engines ; Instructional films ; Nonfiction films ; Internet videos ; Films de formation ; Films autres que de fiction ; Vidéos sur Internet
    Abstract: With an increased demand for SEO effectiveness, Python, and the advances in data science, this course teaches web professionals, data analysts, and scientists how to understand and apply data-driven approaches to improve website search visibility. It's crucial for those in digital marketing and data analysis to stay ahead in a rapidly evolving field where data science is key to success. This course uniquely integrates the power of Python and data science with the strategic needs of Search Engine Optimization. This course offers in-depth knowledge and practical skills for leveraging data science in enhancing SEO strategies across high demand SEO topic areas. Learners will understand how to navigate the challenges of integrating data science techniques into SEO strategies. The course addresses the gap in knowledge between SEO practices and data science, providing professionals with the tools to make informed, data-backed decisions, crucial for enhancing online website visibility in search engines. Learners will acquire skills in querying the Google Search Console API, keyword clustering based on search intent, data-driven content optimization, forecasting search trends, and conducting valid SEO split tests. The course empowers participants to use Python for practical, impactful SEO and data analysis, setting a new standard for excellence in SEO. What you'll learn and how you can apply it By the end of this course, the learner should understand the relationship between data and SEO performance and be able to apply data science techniques in Python. This course is for you because... You're a SEO professional interested in learning to save time producing high impact SEO recommendations. You're a data scientist or data analyst looking to level up your SEO skills to help your organization win and retain more clients and increase visibility. You want to see how Python and Data Science can be applied to SEO. Prerequisites Open a Jupyter notebook Run Python commands in Jupyter Install Python packages either via the command line or Jupyter using the Python Install Packages (pip).
    Note: Online resource; title from title details screen (O'Reilly, viewed January 23, 2024)
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  • 88
    ISBN: 1837633452 , 9781837633456
    Language: English
    Pages: 1 online resource
    Edition: 3rd edition.
    DDC: 005.13/3
    Keywords: Python (Computer program language) ; Machine learning ; Neural networks (Computer science) ; Python (Langage de programmation) ; Apprentissage automatique ; Réseaux neuronaux (Informatique)
    Abstract: Master effective navigation of neural networks, including convolutions and transformers, to tackle computer vision and NLP tasks using Python Key Features Understand the theory, mathematical foundations and the structure of deep neural networks Become familiar with transformers, large language models, and convolutional networks Learn how to apply them on various computer vision and natural language processing problems Purchase of the print or Kindle book includes a free PDF eBook Book Description The field of deep learning has developed rapidly recently and today covers a broad range of applications. This makes it challenging to navigate and hard to understand without solid foundations. This book will guide you from the basics of neural networks to the state-of-the-art large language models in use today. The first part of the book introduces the main machine learning concepts and paradigms. It covers the mathematical foundations, the structure, and the training algorithms of neural networks and dives into the essence of deep learning. The second part of the book introduces convolutional networks for computer vision. We'll learn how to solve image classification, object detection, instance segmentation, and image generation tasks. The third part focuses on the attention mechanism and transformers - the core network architecture of large language models. We'll discuss new types of advanced tasks they can solve, such as chatbots and text-to-image generation. By the end of this book, you'll have a thorough understanding of the inner workings of deep neural networks. You'll have the ability to develop new models and adapt existing ones to solve your tasks. You'll also have sufficient understanding to continue your research and stay up to date with the latest advancements in the field. What you will learn Establish theoretical foundations of deep neural networks Understand convolutional networks and apply them in computer vision applications Become well versed with natural language processing and recurrent networks Explore the attention mechanism and transformers Apply transformers and large language models for natural language and computer vision Implement coding examples with PyTorch, Keras, and Hugging Face Transformers Use MLOps to develop and deploy neural network models Who this book is for This book is for software developers/engineers, students, data scientists, data analysts, machine learning engineers, statisticians, and anyone interested in deep learning. Prior experience with Python programming is a prerequisite.
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  • 89
    Online Resource
    Online Resource
    [Place of publication not identified] : Packt Publishing
    ISBN: 9781837633579 , 1837633576
    Language: English
    Pages: 1 online resource (1 video file (6 hr., 28 min.)) , sound, color.
    Edition: [First edition].
    DDC: 006.3/1
    Keywords: Deep learning (Machine learning) ; Neural networks (Computer science) ; Python (Computer program language) ; Computer programming ; Instructional films ; Nonfiction films ; Internet videos
    Abstract: Are you ready to start your path to becoming a deep learning expert? Then this course is for you. This course is step-by-step. In every new tutorial, we build on what we have already learned and move one extra step forward, and then we assign you a small task that is solved at the beginning of the next video. We start by teaching the theoretical part of the concept, and then implement everything as it is practically using Python. This comprehensive course will be your guide to learning how to use the power of Python to train your machine such that your machine starts learning just like humans, and based on that learning, your machine starts making predictions as well! We will be using Python as a programming language in this course, which is the hottest language nowadays if we talk about machine learning. Python will be taught from the elementary level up to an advanced level so that any machine learning concept can be implemented. You will also learn various steps of data preprocessing, which allows us to make data ready for machine learning algorithms. You will learn the general concepts of machine learning overall, which will be followed by the implementation of one of the essential ML algorithms, "Deep Neural Networks". Each concept of DNNs will be taught theoretically and will be implemented using Python. By the end of this course, you will be able to understand the methodology of DNNs with deep learning using real-world datasets. What You Will Learn Learn the basics of machine learning and neural networks Understand the architecture of neural networks Learn the basics of training a DNN using the Gradient Descent algorithm Learn how to implement a complete DNN using NumPy Learn to create a complete structure for DNN from scratch using Python Work on a project using deep learning for the IRIS dataset Audience This course is designed for anyone who is interested in data science or interested in taking their data-speak to a higher level. Students who want to master DNNs with real datasets in deep learning or who want to implement DNNs in realistic projects can also benefit from the course. You need to have a background in deep learning to get the best out of this course. About The Author AI Sciences: AI Sciences is a group of experts, PhDs, and practitioners of AI, ML, computer science, and statistics. Some of the experts work in big companies such as Amazon, Google, Facebook, Microsoft, KPMG, BCG, and IBM. They have produced a series of courses mainly dedicated to beginners and newcomers on the techniques and methods of machine learning, statistics, artificial intelligence, and data science. Initially, their objective was to help only those who wish to understand these techniques more easily and to be able to start without too much theory. Today, they also publish more complete courses for a wider audience. Their courses have had phenomenal success and have helped more than 100,000 students master AI and data science.
    Note: "Published in January 2023.". - Online resource; title from title details screen (O'Reilly, viewed February 20, 2023)
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  • 90
    Online Resource
    Online Resource
    Shelter Island, NY : Manning Publications Co.
    ISBN: 9781617298417 , 1617298417
    Language: English
    Pages: 1 online resource (xxvi, 412 pages) , illustrations.
    Parallel Title: Erscheint auch als
    DDC: 005.3
    Keywords: Application program interfaces (Computer software) ; Software architecture ; Python (Computer program language)
    Abstract: Strategies, best practices, and patterns that will help you design resilient microservices architecture and streamline your API integrations. In Microservice APIs, you'll discover: Service decomposition strategies for microservices Documentation-driven development for APIs Best practices for designing REST and GraphQL APIs Documenting REST APIs with the OpenAPI specification (formerly Swagger) Documenting GraphQL APIs using the Schema Definition Language Building microservices APIs with Flask, FastAPI, Ariadne, and other frameworks Service implementation patterns for loosely coupled services Property-based testing to validate your APIs, and using automated API testing frameworks like schemathesis and Dredd Adding authentication and authorization to your microservice APIs using OAuth and OpenID Connect (OIDC) Deploying and operating microservices in AWS with Docker and Kubernetes Microservice APIs teaches you practical techniques for designing robust microservices with APIs that are easy to understand, consume, and maintain. You'll benefit from author José Haro Peralta's years of experience experimenting with microservices architecture, dodging pitfalls and learning from mistakes he's made. Inside you'll find strategies for delivering successful API integrations, implementing services with clear boundaries, managing cloud deployments, and handling microservices security. Written in a framework-agnostic manner, its universal principles can easily be applied to your favorite stack and toolset. About the Technology Clean, clear APIs are essential to the success of microservice applications. Well-designed APIs enable reliable integrations between services and help simplify maintenance, scaling, and redesigns. This book teaches you the patterns, protocols, and strategies you need to design, build, and deploy effective REST and GraphQL microservices APIs. About the Book Microservice APIs gathers proven techniques for creating and building easy-to-consume APIs for microservices applications. Rich with proven advice and Python-based examples, this practical book focuses on implementation over philosophy. You'll learn how to build robust microservice APIs, test and protect them, and deploy them to the cloud following principles and patterns that work in any language. What's Inside Service decomposition strategies for microservices Best practices for designing and building REST and GraphQL APIs Service implementation patterns for loosely coupled components API authorization with OAuth and OIDC Deployments with AWS and Kubernetes About the Reader For developers familiar with the basics of web development. Examples are in Python. About the Author José Haro Peralta is a consultant, author, and instructor. He's also the founder of microapis.io. Quotes An insightful guide for creating REST and GraphQL APIs, with neat examples using FastAPI and Flask. The service implementation patterns chapter is a must-read for every developer. - William Jamir Silva, Adjust A perfect introduction to microservice web APIs in Python. - Stuart Woodward, CEO, Hanamaru A well-designed API makes all the difference in the success of your next project. This book equips you with the knowledge and the skills you need. Excellent! - Alain Lompo, ISO-Gruppe Excellent coverage with practical examples. - Sambasiva Andaluri, IBM.
    Note: Includes bibliographical references and index. - Description based on print version record
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  • 91
    Online Resource
    Online Resource
    [Place of publication not identified] : Packt Publishing
    ISBN: 9781803241616 , 1803241616
    Language: English
    Pages: 1 online resource (1 video file (4 hr., 23 min.)) , sound, color.
    Edition: [First edition].
    DDC: 005.13/3
    Keywords: Python (Computer program language) ; Computer programming ; Deep learning (Machine learning) ; Machine learning ; Instructional films ; Nonfiction films ; Internet videos
    Abstract: Welcome to the course where you will learn about the NumPy stack in Python, which is an important prerequisite for deep learning, machine learning, and data science. In this self-paced course, you will learn how to use NumPy, Matplotlib, Pandas, and SciPy to perform critical tasks related to data science and machine learning. This involves performing numerical computation and representing data, visualizing data with plots, loading in, and manipulating data using DataFrames, performing statistics and probability, and building machine learning models for classification and regression. In this course, we will first start with NumPy; we will understand the benefits of NumPy array and then we will look at some complicated matrix operations, such as products, inverses, determinants, and solving linear systems. Then we will cover Matplotlib. In this section, we will go over some common plots, namely the line chart, scatter plot, and histogram. We will also look at how to show images using Matplotlib. Next, we will talk about Pandas. We will look at how much easier it is to load a dataset using Pandas versus trying to do it manually. Then we will look at some data frame operations useful in machine learning, such as filtering by column, filtering by row, and the apply function. Later, you will learn about SciPy. In this section, you will learn how to do common statistics calculations, including getting the PDF value, the CDF value, sampling from a distribution, and statistical testing. Finally, we will also cover some basics of machine learning that will help us start our deep learning journey. By the end of the course, we will be able to confidently use the NumPy stack in deep learning and data science. What You Will Learn Understand supervised machine learning with real-world examples Understand and code using the NumPy stack Make use of NumPy, SciPy, Matplotlib, and Pandas to implement numerical algorithms Understand the pros and cons of various machine learning models Get a brief introduction to the classification and regression Learn how to calculate the PDF and CDF under the normal distribution Audience This course is designed for anyone who is interested in data science and machine learning, who knows Python and wants to take the next step into Python libraries for data science, or who is interested in acquiring tools to implement machine learning algorithms. One must have decent Python programming skills and a basic understanding of linear algebra and probability for this course. About The Author Lazy Programmer: The Lazy Programmer is an AI and machine learning engineer with a focus on deep learning, who also has experience in data science, big data engineering, and full-stack software engineering. With a background in computer engineering and specialization in machine learning, he holds two master's degrees in computer engineering and statistics with applications to financial engineering. His expertise in online advertising and digital media includes work as both a data scientist and big data engineer. He has created deep learning models for prediction and has experience in recommendation systems using reinforcement learning and collaborative filtering. He is a skilled instructor who has taught at universities including Columbia, NYU, Hunter College, and The New School. He has web programming expertise, with experience in technologies such as Python, Ruby/Rails, PHP, and Angular, and has provided his services to multiple businesses.
    Note: Online resource; title from title details screen (O'Reilly, viewed April 11, 2023)
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  • 92
    ISBN: 9781484287545 , 1484287541
    Language: English
    Pages: 1 online resource (716 pages) , illustrations
    Edition: Second edition.
    Parallel Title: Erscheint auch als
    Keywords: Decision making Data processing ; Business planning Data processing ; R (Computer program language) ; Python (Computer program language) ; Electronic books
    Abstract: This book illustrates how data can be useful in solving business problems. It explores various analytics techniques for using data to discover hidden patterns and relationships, predict future outcomes, optimize efficiency and improve the performance of organizations. You’ll learn how to analyze data by applying concepts of statistics, probability theory, and linear algebra. In this new edition, both R and Python are used to demonstrate these analyses. Practical Business Analytics Using R and Python also features new chapters covering databases, SQL, Neural networks, Text Analytics, and Natural Language Processing. Part one begins with an introduction to analytics, the foundations required to perform data analytics, and explains different analytics terms and concepts such as databases and SQL, basic statistics, probability theory, and data exploration. Part two introduces predictive models using statistical machine learning and discusses concepts like regression, classification, and neural networks. Part three covers two of the most popular unsupervised learning techniques, clustering and association mining, as well as text mining and natural language processing (NLP). The book concludes with an overview of big data analytics, R and Python essentials for analytics including libraries such as pandas and NumPy. Upon completing this book, you will understand how to improve business outcomes by leveraging R and Python for data analytics.
    Note: Includes bibliographical references and index
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  • 93
    ISBN: 9781803230603
    Language: English
    Pages: 1 online resource (764 pages) , illustrations
    Edition: Second edition.
    DDC: 006.7
    Keywords: Django (Electronic resource) ; Web site development ; Python (Computer program language) ; Electronic books
    Abstract: Do you want to develop reliable and secure applications that stand out from the crowd without spending hours on boilerplate code? You've made the right choice trusting the Django framework, and this book will tell you why. Often referred to as a “batteries included” web development framework, Django comes with all the core features needed to build a standalone application. Web Development with Django will take you through all the essential concepts and help you explore its power to build real-world applications using Python. Throughout the book, you'll get the grips with the major features of Django by building a website called Bookr – a repository for book reviews. This end-to-end case study is split into a series of bitesize projects presented as exercises and activities, allowing you to challenge yourself in an enjoyable and attainable way. As you advance, you'll acquire various practical skills, including how to serve static files to add CSS, JavaScript, and images to your application, how to implement forms to accept user input, and how to manage sessions to ensure a reliable user experience. You'll cover everyday tasks that are part of the development cycle of a real-world web application. By the end of this Django book, you'll have the skills and confidence to creatively develop and deploy your own projects.
    Note: Includes index
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  • 94
    Online Resource
    Online Resource
    [Place of publication not identified] : Manning Publications
    Language: English
    Pages: 1 online resource (1 video file (17 hr., 37 min.)) , sound, color.
    Edition: [First edition].
    DDC: 005.13/3
    Keywords: Python (Computer program language) Study guides ; Computer programming Study guides ; Computer programming ; Python (Computer program language) ; Instructional films ; Internet videos ; Nonfiction films ; Study guides ; Instructional films ; Nonfiction films ; Internet videos
    Abstract: Python is the #1 programming language for beginners due to its simple syntax, wide range of applications, and helpful community resources. This course, intended for absolute beginners, will start you on your coding journey with Python 3, the most recent version of the language. We start by setting up a Python environment and learning programming basics, then move on to cover object-oriented programming, regular expressions, interacting with HTTP (Hypertext Transfer Protocol), networking and more. Enroll now to receive over 17 hours of HD video tutorials with English captions, and a certificate of completion! Python calculator After learning Python basics, we will code our first program, a calculator. Web scraping We will code a simple web scraper to extract data rom a web site. This will demonstrate how to use Python to interact with HTTP. Chat program We will code a chat program using another communication protocol, WebSockets, to learn the basics of networking with Python.
    Note: Online resource; title from title details screen (O'Reilly, viewed June 26, 2023)
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  • 95
    ISBN: 9788383223247 , 8383223242
    Language: Polish
    Pages: 1 online resource (504 pages)
    Edition: Wydanie III.
    Uniform Title: Python for data analysis
    DDC: 005.13/3
    Keywords: Python (Computer program language) ; Programming languages (Electronic computers) ; Data mining ; Data mining ; Programming languages (Electronic computers) ; Python (Computer program language)
    Abstract: Wprawny analityk danych potrafi z nich uzyskać wiedzę ułatwiającą podejmowanie trafnych decyzji. Od kilku lat można do tego używać nowoczesnych narzędzi Pythona, które zbudowano specjalnie do tego celu. Praca z nimi nie wymaga głębokiej znajomości statystyki czy algebry. Aby cieszyć się uzyskanymi rezultatami, wystarczy się wprawić w stosowaniu kilku pakietów i środowisk Pythona. Ta książka jest trzecim, starannie zaktualizowanym wydaniem wyczerpującego przewodnika po narzędziach analitycznych Pythona. Uwzględnia Pythona 3.0 i bibliotekę̜ pandas 1.4. Została napisana w przystępny sposób, a poszczególne zagadnienia bogato zilustrowano przykładami, studiami rzeczywistych przypadków i fragmentami kodu. W trakcie lektury nauczysz się korzystać z możliwości oferowanych przez pakiety pandas i NumPy, a także środowiska IPython i Jupyter. Nie zabrakło wskazówek dotyczących używania uniwersalnych narzędzi przeznaczonych do ładowania, czyszczenia, przekształcania i łączenia zbiorów danych. Pozycję docenią analitycy zamierzający zacząć pracę w Pythonie, jak również programiści Pythona, którzy chcą się zająć analizą danych i obliczeniami naukowymi.
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  • 96
    Online Resource
    Online Resource
    [Place of publication not identified] : Ascent Audio
    ISBN: 9781663721112 , 1663721114
    Language: English
    Pages: 1 online resource (1 audio file (13 hr., 2 min.))
    Edition: Second edition.
    DDC: 005.133
    Keywords: Python (Computer program language) ; Application software Development ; Audiobooks
    Abstract: Easy to understand and engaging, this updated edition of Introducing Python is ideal for beginning programmers as well as those new to the language. Author Bill Lubanovic takes you from the basics to more involved and varied topics, mixing tutorials with cookbook-style code recipes to explain concepts in Python 3. End-of-chapter exercises help you practice what you've learned. You'll gain a strong foundation in the language, including best practices for testing, debugging, code reuse, and other development tips. This book also shows you how to use Python for applications in business, science, and the arts, using various Python tools and open source packages.
    Note: Online resource; title from title details screen (O'Reilly, viewed May 2, 2023)
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  • 97
    ISBN: 9781837633517 , 1837633517 , 9781837637553
    Language: English
    Pages: 1 online resource (587 p.)
    Edition: 3rd ed.
    Series Statement: Expert insight
    DDC: 005.8
    Keywords: Python (Computer program language) ; Computer security ; Computer networks Security measures ; HTTP (Computer network protocol) Programming ; Electronic books
    Abstract: Python’s latest updates add numerous libraries that can be used to perform critical security-related missions, including detecting vulnerabilities in web applications, taking care of attacks, and helping to build secure and robust networks that are resilient to them. This fully updated third edition will show you how to make the most of them and improve your security posture. The first part of this book will walk you through Python scripts and libraries that you’ll use throughout the book. Next, you’ll dive deep into the core networking tasks where you will learn how to check a network’s vulnerability using Python security scripting and understand how to check for vulnerabilities in your network – including tasks related to packet sniffing. You’ll also learn how to achieve endpoint protection by leveraging Python packages along with writing forensics scripts. The next part of the book will show you a variety of modern techniques, libraries, and frameworks from the Python ecosystem that will help you extract data from servers and analyze the security in web applications. You’ll take your first steps in extracting data from a domain using OSINT tools and using Python tools to perform forensics tasks. By the end of this book, you will be able to make the most of Python to test the security of your network and applications.
    Note: Description based upon print version of record. - Implementing secure sockets with the TLS and SSL modules. - Includes bibliographical references and index
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  • 98
    Online Resource
    Online Resource
    [Place of publication not identified] : Manning Publications
    Language: English
    Pages: 1 online resource (1 video file (6 hr., 1 min.)) , sound, color.
    Edition: [First edition].
    DDC: 005.13/3
    Keywords: Python (Computer program language) ; Machine learning ; Instructional films ; Nonfiction films ; Internet videos
    Abstract: This A to Z course introduces newcomers to the world of data science and teaches the fundamental skills for using machine learning and artificial intelligence (AI) to glean meaning and insights from data. It covers Python's data types and shows how to use the must-have Python data science libraries, including Pandas for data analysis and Matplotlib for creating visuals of the results. Once you understand how to format and clean your data and perform exploratory data analysis, we move to the machine learning side. Here, we introduce you to supervised vs unsupervised learning, as well as the core algorithms, including simple and multiple linear regression. We finish up with a deep dive into a recommender system for movies, and a chance to put together all your new skills and knowledge. Each topic is described in plain English, and the course does its best to avoid mathematical notations and jargon. Once you have access to the source code, you can experiment with it and improve upon it, learning and applying these algorithms in the real world. The data science field is lucrative and growing. This course will introduce you to all the foundational skills that a data scientist must have. If you have no background in statistics, don't let that stop you from enrolling in this course!.
    Note: Online resource; title from title details screen (O'Reilly, viewed June 13, 2023)
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  • 99
    Online Resource
    Online Resource
    [Place of publication not identified] : Packt Publishing
    ISBN: 9781835083857 , 1835083854
    Language: English
    Pages: 1 online resource (1 video file (7 hr., 51 min.)) , sound, color.
    Edition: [First edition].
    DDC: 332.028541
    Keywords: Artificial intelligence Financial applications ; Python (Computer program language) ; Instructional films ; Nonfiction films ; Internet videos
    Abstract: In this course, you will learn how to develop an end-to-end decentralized trading bot using Python, interact with Telegram for real-time notifications, and leverage the capabilities of AWS to run your code 24/7 in the cloud. The course begins with an introduction to the objectives and expectations, providing a clear roadmap for you. You will explore the concepts of statistical arbitrage and cointegration and understand how to trade the spread based on the Z-score. Dive into the nuances of hedge ratio and half-life and discover how to apply the Kelly Criterion for optimal position sizing and risk management. DYDX FastTrack empowers you to seamlessly configure MetaMask, Alchemy HTTP provider, and access DYDX credentials. Interact with the DYDX API for public price data retrieval and placing orders through the private API. This comprehensive guide includes environment setup in VS Code, Python VENV setup, and creating a collaborative GitHub repository. Master the integration process with environment variables for flawless operations. In the Bot Build stages, you will configure bot constants, construct market prices, and implement cointegration functions. Explore the BotAgent class, which forms the backbone of your trading bot, and effectively manage open trades and exits. Finally, deploy your bot on the AWS cloud, set up real-time Telegram updates, and achieve full automation with CRON. By the end of this course, you will be equipped with the knowledge and skills to develop and deploy an advanced decentralized trading bot on DYDX, leveraging the power of Python and AWS. What You Will Learn Create a statistical arbitrage strategy based on cointegration Implement position sizing and risk management techniques for successful trading Learn how to connect and access DYDX API for trading Understand the concept of z-score and its application in trading spreads Explore Telegram messaging integration for real-time trade notifications Gain proficiency in deploying your bot on AWS for continuous trading Audience This course is ideal for intermediate to advanced Python developers and cryptocurrency enthusiasts aiming to enhance their trading abilities on the DYDX platform. Basic proficiency in Python is required for in-depth coding and bot development. While prior knowledge of cryptocurrency trading strategies is beneficial, it is not mandatory. Access to a computer with internet connectivity is necessary to set up the development environment and interact with DYDX. Familiarity with blockchain technology and decentralized exchanges is recommended for better comprehension. About The Author XCHAIN ANALYTICS LTD: XCHAIN ANALYTICS LTD is a leading analyst and full-stack developer with expertise in Python and React. Their passion lies in discovering and promoting innovative ideas that are often overlooked in the hype-driven tech industry. With a focus on authenticity and practicality, XCHAIN ANALYTICS strives to share their knowledge and empower others through teaching. They believe in the power of continuous learning and experimentation and often find themselves exploring new ventures. However, they always return to teaching as their true calling, where they can put their ideas into action and help others effectively.
    Note: "Published in June 2023.". - Online resource; title from title details screen (O'Reilly, viewed August 7, 2023)
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  • 100
    Online Resource
    Online Resource
    [Place of publication not identified] : Packt Publishing
    ISBN: 9781835085400 , 1835085407
    Language: English
    Pages: 1 online resource (1 video file (3 hr., 32 min.)) , sound, color.
    Edition: [First edition].
    DDC: 658.4/038011
    Keywords: Microsoft Power BI (Computer file) ; Business intelligence Computer programs ; Information visualization Computer programs ; Dashboards (Management information systems) ; Python (Computer program language) ; Instructional films ; Nonfiction films ; Internet videos
    Abstract: Microsoft Power BI is a powerful business analytics/data visualization tool that enables users to connect to various data sources, create interactive reports and dashboards, and gain insights from the data. With its intuitive interface and robust features, Power BI empowers users to make data-driven decisions and effectively communicate information with stakeholders. The course begins with setting up the Python environment for Power BI and then dives into using Python for data visualization in Power BI. It covers advanced DAX expressions and calculations, exploring a business dataset. You will also learn how to leverage Python libraries for data science in Power BI and create a balance sheet structure. The course includes applying custom visuals and culminates in creating a final dashboard. However, the Python concepts covered in this course cannot be considered comprehensive, only content related to Power BI usage requirements. Upon completing the course, you will gain advanced knowledge and skills in using Power BI for data visualization, analysis, and science. You will be proficient in setting up the Python environment in Power BI, leveraging Python for data visualization, and applying advanced DAX expressions and calculations, enabling you to effectively analyze/present data in various business scenarios. What You Will Learn Learn to integrate Python for data visualization in Power BI Master advanced DAX expressions and calculations Apply custom visuals to enhance your reporting capabilities Create balance sheet structures and evaluate balances using DAX Utilize Python's data science libraries for analysis in Power BI Build a comprehensive dashboard for effective data presentation Audience This course suits those interested in combining Power BI and Python and anyone wanting to explore Microsoft Power BI. If you want to excel in your work, explore new data analytics career opportunities, or lay the foundation for a successful future in the data era, this course equips you with the necessary skills. It also appeals to individuals seeking a better tool than QlikView and IBM Cognos. Prerequisites include Power BI, free to download with no additional costs, and a willingness to learn and explore new opportunities; all necessary files and materials will be provided. About The Author Dan We: Daniel Weikert is a 33-year-old entrepreneur, data enthusiast, consultant, and trainer. He is a master's degree holder certified in Power BI, Tableau, Alteryx (Core and Advanced), and KNIME (L1-L3). He is currently working in the business intelligence field and helps companies and individuals obtain vital insights from their data to deliver long-term strategic growth and outpace their competitors. He has a passion for learning and teaching. He is committed to supporting other people by offering them educational services and helping them accomplish their goals, gain expertise in their profession, or explore new careers.
    Note: Online resource; title from title details screen (O'Reilly, viewed August 3, 2023)
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