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  • 1
    Online Resource
    Online Resource
    Python machine learning : machine learning and deep learning with Python, scikit-learn, and TensorFlow (2017)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781787126022 , 1787126021
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Edition: Second edition, fully revised and updated.
    Keywords: Python (Computer program language) ; Machine learning ; Electronic books ; Electronic books ; local
    Abstract: Unlock modern machine learning and deep learning techniques with Python by using the latest cutting-edge open source Python libraries. About This Book Second edition of the bestselling book on Machine Learning A practical approach to key frameworks in data science, machine learning, and deep learning Use the most powerful Python libraries to implement machine learning and deep learning Get to know the best practices to improve and optimize your machine learning systems and algorithms Who This Book Is For If you know some Python and you want to use machine learning and deep learning, pick up this book. Whether you want to start from scratch or extend your machine learning knowledge, this is an essential and unmissable resource. Written for developers and data scientists who want to create practical machine learning and deep learning code, this book is ideal for developers and data scientists who want to teach computers how to learn from data. What You Will Learn Understand the key frameworks in data science, machine learning, and deep learning Harness the power of the latest Python open source libraries in machine learning Explore machine learning techniques using challenging real-world data Master deep neural network implementation using the TensorFlow library Learn the mechanics of classification algorithms to implement the best tool for the job Predict continuous target outcomes using regression analysis Uncover hidden patterns and structures in data with clustering Delve deeper into textual and social media data using sentiment analysis In Detail Machine learning is eating the software world, and now deep learning is extending machine learning. Understand and work at the cutting edge of machine learning, neural networks, and deep learning with this second edition of Sebastian Raschka's bestselling book, Python Machine Learning. Thoroughly updated using the latest Python open source libraries, this book offers the practical knowledge and techniques you need to create and contribute to machine learning, deep learning, and modern data analysis. Fully extended and modernized, Python Machine Learning Second Edition now includes the popular TensorFlow deep learning library. The scikit-learn code has also been fully updated to include recent improvements and additions to this versatile machine learning library. Sebastian Raschka and Vahid Mirjalili's unique insight and expertise introduce you to machine learning and deep learning algorithms from s...
    Note: Previous edition published: 2015. - Includes index. - Description based on online resource; title from cover (Safari, viewed October 18, 2017)
    URL: https://learning.oreilly.com/library/view/-/9781787125933/?ar
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  • 2
    Online Resource
    Online Resource
    Python : deeper insights into machine learning : leverage benefits of machine learning techniques using Python : a course in three modules (2016)
    Birmingham, UK : Packt Publishing
    Details
    Language: English
    Pages: 1 online resource (1 volume) , illustrations.
    Series Statement: Learning path
    Keywords: Machine learning ; Python (Computer program language) ; Electronic books ; Electronic books ; local
    Abstract: Leverage benefits of machine learning techniques using Python. About This Book Improve and optimise machine learning systems using effective strategies. Develop a strategy to deal with a large amount of data. Use of Python code for implementing a range of machine learning algorithms and techniques. Who This Book Is For This title is for data scientist and researchers who are already into the field of data science and want to see machine learning in action and explore its real-world application. Prior knowledge of Python programming and mathematics is must with basic knowledge of machine learning concepts. What You Will Learn Learn to write clean and elegant Python code that will optimize the strength of your algorithms Uncover hidden patterns and structures in data with clustering Improve accuracy and consistency of results using powerful feature engineering techniques Gain practical and theoretical understanding of cutting-edge deep learning algorithms Solve unique tasks by building models Get grips on the machine learning design process In Detail Machine learning and predictive analytics are becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. It is one of the fastest growing trends in modern computing, and everyone wants to get into the field of machine learning. In order to obtain sufficient recognition in this field, one must be able to understand and design a machine learning system that serves the needs of a project. The idea is to prepare a learning path that will help you to tackle the real-world complexities of modern machine learning with innovative and cutting-edge techniques. Also, it will give you a solid foundation in the machine learning design process, and enable you to build customized machine learning models to solve unique problems. The course begins with getting your Python fundamentals nailed down. It focuses on answering the right questions that cove a wide range of powerful Python libraries, including scikit-learn Theano and Keras.After getting familiar with Python core concepts, it's time to dive into the field of data science. You will further gain a solid foundation on the machine learning design and also learn to customize models for solving problems. At a later stage, you will get a grip on more advanced techniques and acquire a broad set of powerful skills in the area of feature selection and feature engineering. Style and approach This course includes all the resource...
    Note: Authors: Sebastian Raschka, David Julian, John Hearty. Cf. Credits page. - Includes bibliographical references and index. - Description based on online resource; title from title page (Safari, viewed October 3, 2016)
    URL: https://learning.oreilly.com/library/view/-/9781787128576/?ar
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    Online Resource
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  • 3
    Online Resource
    Online Resource
    Python Machine Learning - Third Edition (2019)
    [Erscheinungsort nicht ermittelbar] : Packt Publishing | Boston, MA : Safari
    Details
    Language: English
    Pages: 1 online resource (770 pages)
    Edition: 3rd edition
    Keywords: Electronic books ; local
    Abstract: Applied machine learning with a solid foundation in theory. Revised and expanded for TensorFlow 2, GANs, and reinforcement learning. Key Features Third edition of the bestselling, widely acclaimed Python machine learning book Clear and intuitive explanations take you deep into the theory and practice of Python machine learning Fully updated and expanded to cover TensorFlow 2, Generative Adversarial Network models, reinforcement learning, and best practices Book Description Python Machine Learning, Third Edition is a comprehensive guide to machine learning and deep learning with Python. It acts as both a step-by-step tutorial, and a reference you'll keep coming back to as you build your machine learning systems. Packed with clear explanations, visualizations, and working examples, the book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, Raschka and Mirjalili teach the principles behind machine learning, allowing you to build models and applications for yourself. Updated for TensorFlow 2.0, this new third edition introduces readers to its new Keras API features, as well as the latest additions to scikit-learn. It's also expanded to cover cutting-edge reinforcement learning techniques based on deep learning, as well as an introduction to GANs. Finally, this book also explores a subfield of natural language processing (NLP) called sentiment analysis, helping you learn how to use machine learning algorithms to classify documents. This book is your companion to machine learning with Python, whether you're a Python developer new to machine learning or want to deepen your knowledge of the latest developments. What you will learn Master the frameworks, models, and techniques that enable machines to 'learn' from data Use scikit-learn for machine learning and TensorFlow for deep learning Apply machine learning to image classification, sentiment analysis, intelligent web applications, and more Build and train neural networks, GANs, and other models Discover best practices for evaluating and tuning models Predict continuous target outcomes using regression analysis Dig deeper into textual and social media data using sentiment analysis Who This Book Is For If you know some Python and you want to use machine learning and deep learning, pick up this book. Whether you want to start from scratch or extend your machine learning knowledge, this is an essential ...
    Note: Online resource; Title from title page (viewed December 12, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781789955750/?ar
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  • 4
    Online Resource
    Online Resource
    Python machine learning : unlock deeper insights into machine learning with this vital guide to cutting-edge predictive analytics (2015)
    Birmingham : Packt Publishing
    Details
    ISBN: 1783555149 , 9781783555130 , 9781783555147
    Language: English
    Pages: Online Resource (454 Seiten) , Illustrationen
    Edition: Online-Ausg.
    Series Statement: Safari Tech Books Online
    Series Statement: Community experience distilled
    Parallel Title: Print version Python Machine Learning
    DDC: 005.13/3
    Keywords: Python (Computer program language) ; Machine learning
    Note: Parallel als Druckausgabe erschienen , Description based on online resource; title from cover page (Safari, viewed October 12, 2015) , Includes index
    URL: Volltext
    URL: Volltext
    URL: Volltext
    URL: https://learning.oreilly.com/library/view/-/9781783555130/?ar
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  • 5
    Online Resource
    Online Resource
    Machine Learning mit Python und Scikit-learn und TensorFlow : das umfassende Praxis-Handbuch für Data Science, Deep Learning und Predictive Analytics (2018)
    [Frechen, Germany] : mitp Verlags
    Details
    ISBN: 9783958457355 , 3958457355
    Language: German
    Pages: 1 online resource (1 volume) , illustrations
    Edition: 2. Auflage.
    Keywords: Machine learning ; Python (Computer program language) ; Data mining ; Electronic books ; Electronic books ; local
    Abstract: Datenanalyse mit ausgereiften statistischen Modellen des Machine Learnings Anwendung der wichtigsten Algorithmen und Python-Bibliotheken wie NumPy, SciPy, Scikit-learn, TensorFlow, Matplotlib, Pandas und Keras Best Practices zur Optimierung Ihrer Machine-Learning-Algorithmen Machine Learning und Predictive Analytics verändern die Arbeitsweise von Unternehmen grundlegend. Die Fähigkeit, in komplexen Daten Trends und Muster zu erkennen, ist heutzutage für den langfristigen geschäftlichen Erfolg ausschlaggebend und entwickelt sich zu einer der entscheidenden Wachstumsstrategien. Die zweite Auflage dieses Buchs berücksichtigt die jüngsten Entwicklungen und Technologien, die für Machine Learning, Neuronale Netze und Deep Learning wichtig sind. Dies betrifft insbesondere die neuesten Open-Source-Bibliotheken wie Scikit-learn, Keras und TensorFlow. Python zählt zu den führenden Programmiersprachen in den Bereichen Machine Learning, Data Science und Deep Learning und ist besonders gut dazu geeignet, grundlegende Erkenntnisse aus Ihren Daten zu gewinnen sowie ausgefeilte Algorithmen und statistische Modelle auszuarbeiten, die neue Einsichten liefern und wichtige Fragen beantworten. Die Autoren erläutern umfassend den Einsatz von Machine-Learning- und Deep-Learning-Algorithmen und wenden diese anhand zahlreicher Beispiele praktisch an. Dafür behandeln sie in diesem Buch ein breites Spektrum leistungsfähiger Python-Bibliotheken wie Scikit-learn, Keras und TensorFlow. Sie lernen detailliert, wie Sie Python für maschinelle Lernverfahren einsetzen und dabei eine Vielzahl von statistischen Modellen verwenden. Aus dem Inhalt: Trainieren von Lernalgorithmen für die Klassifizierung Regressionsanalysen zum Prognostizieren von Ergebnissen Clusteranalyse zum Auffinden verborgener Muster und Strukturen in Ihren Daten Deep-Learning-Verfahren zur Bilderkennung Optimale Organisation Ihrer Daten durch effektive Verfahren zur Vorverarbeitung Datenkomprimierung durch Dimensionsreduktion Training Neuronaler Netze mit TensorFlow Kombination verschiedener Modelle für das Ensemble Learning Einbettung eines Machine-Learning-Modells in eine Webanwendung Stimmungsanalyse in Social Networks Modellierung sequenzieller Daten durch rekurrente Neuronale Netze
    Note: "First published in the English language under the title Python machine learning, Second edition (9781787125933)"--Copyright page. - Description based on online resource; title from title page (Safari, viewed January 16, 2018)
    URL: lizenzpflichtig
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  • 6
    Online Resource
    Online Resource
    Machine Learning mit Python : das Praxis-Handbuch für Data Science, Predictive Analytics und Deep Learning (2017)
    [Germany] : mitp Verlags
    Details
    ISBN: 9783958454224 , 3958454224 , 9783958454248 , 3958454240
    Language: German
    Pages: 1 online resource (1 volume) , illustrations
    Edition: 1. Auflage.
    Keywords: Machine learning ; Python (Computer program language) ; Data mining ; Electronic books ; Electronic books ; local
    Abstract: Datenanalyse mit ausgereiften statistischen Modellen des Machine Learnings Anwendung der wichtigsten Algorithmen und Python-Bibliotheken wie NumPy, SciPy, scikit-learn, matplotlib, pandas, Theano und Keras Verständlicher und eleganter Python-Code zur Optimierung Ihrer Algorithmen Machine Learning und Predictive Analytics verändern die Arbeitsweise von Unternehmen grundlegend. Die Fähigkeit, in komplexen Daten Trends und Muster zu erkennen, ist heutzutage für den langfristigen geschäftlichen Erfolg ausschlaggebend und entwickelt sich zu einer der entscheidenden Wachstumsstrategien. Sebastian Raschka gibt Ihnen einen detaillierten Einblick in die Techniken der Predictive Analytics. Er erläutert die grundlegenden theoretischen Prinzipien des Machine Learnings und wendet sie praktisch an. Dabei konzentriert er sich insbesondere auf das Stellen und Beantworten der richtigen Fragen. Python zählt zu den führenden Programmiersprachen im Bereich Data Science und ist besonders gut dazu geeignet, grundlegende Erkenntnisse aus Ihren Daten zu gewinnen sowie ausgefeilte Algorithmen und statistische Modelle auszuarbeiten, die neue Einsichten liefern und wichtige Fragen beantworten. Der Autor erläutert in diesem Buch ein breites Spektrum leistungsfähiger Python-Bibliotheken wie scikit-learn, Theano oder Keras. Sie lernen Schritt für Schritt die Grundlagen von Python für maschinelle Lernverfahren kennen und setzen dabei eine Vielfalt von statistischen Modellen ein. Aus dem Inhalt: Regressionsanalysen zum Prognostizieren von Ergebnissen Clusteranalysen zum Auffinden verborgener Muster und Strukturen in Ihren Daten Optimale Organisation Ihrer Daten durch effektive Verfahren zur Vorverarbeitung Datenkomprimierung durch Dimensionsreduktion Neuronale Netze erzeugen mit Keras und Theano Kombination verschiedener Modelle für das Ensemble Learning Einbettung eines Machine-Learning-Modells in eine Webanwendung Stimmungsanalyse in Social Networks
    Note: "First published in the English language under the title Python machine learning - (9781783555130)"--Copyright page. - Description based on online resource; title from title page (Safari, viewed January 5, 2017)
    URL: lizenzpflichtig
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  • 7
    Online Resource
    Online Resource
    Machine Learning mit Python und Keras, TensorFlow 2 und Scikit-Learn (2021)
    [Erscheinungsort nicht ermittelbar] : mitp Verlag | Boston, MA : Safari
    Details
    ISBN: 9783747502150
    Language: English , German
    Pages: 1 online resource (768 pages)
    Edition: 3rd edition
    Keywords: Electronic books ; local
    Abstract: Datenanalyse mit ausgereiften statistischen Modellen des Machine Learnings Anwendung der wichtigsten Algorithmen und Python-Bibliotheken wie NumPy, SciPy, Scikit-learn, Keras, TensorFlow 2, Pandas und Matplotlib Best Practices zur Optimierung Ihrer Machine-Learning-Algorithmen Mit diesem Buch erhalten Sie eine umfassende Einführung in die Grundlagen und den effektiven Einsatz von Machine-Learning- und Deep-Learning-Algorithmen und wenden diese anhand zahlreicher Beispiele praktisch an. Dafür setzen Sie ein breites Spektrum leistungsfähiger Python-Bibliotheken ein, insbesondere Keras, TensorFlow 2 und Scikit-learn. Auch die für die praktische Anwendung unverzichtbaren mathematischen Konzepte werden verständlich und anhand zahlreicher Diagramme anschaulich erläutert. Die dritte Auflage dieses Buchs wurde für TensorFlow 2 komplett aktualisiert und berücksichtigt die jüngsten Entwicklungen und Technologien, die für Machine Learning, Neuronale Netze und Deep Learning wichtig sind. Dazu zählen insbesondere die neuen Features der Keras-API, das Synthetisieren neuer Daten mit Generative Adversarial Networks (GANs) sowie die Entscheidungsfindung per Reinforcement Learning. Ein sicherer Umgang mit Python wird vorausgesetzt. Aus dem Inhalt: Trainieren von Lernalgorithmen und Implementierung in Python Gängige Klassifikationsalgorithmen wie Support Vector Machines (SVM), Entscheidungsbäume und Random Forest Natural Language Processing zur Klassifizierung von Filmbewertungen Clusteranalyse zum Auffinden verborgener Muster und Strukturen in Ihren Daten Deep-Learning-Verfahren für die Bilderkennung Datenkomprimierung durch Dimensionsreduktion Training Neuronaler Netze und GANs mit TensorFlow 2 Kombination verschiedener Modelle für das Ensemble Learning Einbettung von Machine-Learning-Modellen in Webanwendungen Stimmungsanalyse in Social Networks Modellierung sequenzieller Daten durch rekurrente Neuronale Netze Reinforcement Learning und Implementierung von Q-Learning-Algorithmen
    Note: Online resource; Title from title page (viewed March 3, 2021) , Mode of access: World Wide Web.
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  • 8
    Online Resource
    Online Resource
    Praxiseinstieg Machine Learning mit Scikit-Learn und TensorFlow : Konzepte, Tools und Techniken für intelligente Systeme (2018)
    Heidelberg : O'Reilly
    Details
    Language: German
    Pages: 1 online resource (1 volume) , illustrations
    Edition: 1. Auflage.
    Keywords: Machine learning ; Data mining ; Electronic books ; Electronic books ; local
    Abstract: Eine Reihe technischer Durchbrüche beim Deep Learning haben das gesamte Gebiet des maschinellen Lernens in den letzten Jahren beflügelt. Inzwischen können sogar Programmierer, die kaum etwas über diese Technologie wissen, mit einfachen, effizienten Werkzeugen Machine-Learning-Programme implementieren. Dieses praxisorientierte Buch zeigt Ihnen wie.Mit konkreten Beispielen, einem Minimum an Theorie und zwei unmittelbar anwendbaren Python-Frameworks - Scikit-Learn und TensorFlow - verhilft Ihnen der Autor Aurélien Géron zu einem intuitiven Verständnis der Konzepte und Tools für das Entwickeln intelligenter Systeme. Sie lernen eine Vielzahl von Techniken kennen, beginnend mit einfacher linearer Regression bis hin zu Deep Neural Networks. Die in jedem Kapitel enthaltenen Übungen helfen Ihnen, das Gelernte in die Praxis umzusetzen. Um direkt zu starten, benötigen Sie lediglich etwas Programmiererfahrung.
    Note: Authorized German translation of the English edition of: Hands-on machine learning with Scikit-Learn and TensorFlow : concepts, tools, and techniques to build intelligent systems. O'Reilly Media, ©2017. Cf. Title page verso. - Includes bibliographical references and index. - Description based on online resource; title from title page (Safari, viewed February 13, 2018)
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  • 9
    Online Resource
    Online Resource
    Machine Learning with Pytorch and Scikit-Learn : Develop Machine Learning and Deep Learning Models with Python (2022)
    Birmingham : Packt Publishing, Limited
    Details
    ISBN: 1801816387 , 9781801816380
    Language: English
    Pages: 1 online resource (771 p.)
    DDC: 005.13/3
    Keywords: Python (Computer program language) ; Machine learning ; Data mining ; Data Mining ; Python (Langage de programmation) ; Apprentissage automatique ; Exploration de données (Informatique) ; Data mining ; Machine learning ; Python (Computer program language) ; Electronic books
    Note: Description based upon print version of record
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  • 10
    Online Resource
    Online Resource
    The Generative AI Conference : practical insights and proven playbooks (2023)
    [Place of publication not identified] : Packt Publishing
    Details
    ISBN: 9781835884041 , 1835884040
    Language: English
    Pages: 1 online resource (1 video file (6 hr., 30 min.)) , sound, color.
    Edition: [First edition].
    DDC: 006.3
    Keywords: Artificial intelligence Congresses ; Instructional films ; Nonfiction films ; Internet videos
    Abstract: Generative AI is dominating discussions on AI. It is a superpower that every tech professional must harness, but harnessing its power takes knowledge, strategy, and vision. We present you with the recordings from our acclaimed Generative AI conference in one comprehensive video collection. We've packaged the invaluable insights and techniques shared by bestselling AI authors, innovators, and practitioners from Meta, Microsoft, Deloitte, JPMorgan, NVIDIA, Salesforce, and more. These talks and tech sessions provide unique perspectives on realizing generative AI's immense potential. Let the brightest minds equip you with the knowledge to help you put generative AI to work for your unique needs. What you will learn Panel Discussion: Put Generative AI to Work! Navigating the Hype and Shaping the Future with Gen AI Revelations from 30 AI Visionaries About Generative AI's Future in Business The Large Language Model Revolution in Recommender Systems Personalization Fireside Chat: Navigating Responsible AI Development Amidst LLMs Innovation Tackling OWASP's Top 10 Risks Head On Audience This video collection caters to forward-thinking technology professionals and leaders in various industries who aspire to leverage generative AI for business transformation. It is designed for product leaders interested in integrating generative AI into their overall business strategy, AI specialists and researchers in search of the latest techniques for developing custom generative models, data scientists aiming to uncover insights and automate processes using generative AI, as well as business owners and entrepreneurs exploring new revenue opportunities powered by generative AI. About the Authors Clint Bodungen: Clint Bodungen is a globally recognized cybersecurity authority and brings over a quarter-century of experience to the table. A veteran of the United States Air Force and seasoned professional at notable cybersecurity firms like Symantec, Kaspersky Lab, and Booz Allen Hamilton, he is renowned for his innovative approaches in the field. Clint has contributed to the field as the author of two insightful books: 'Hacking Exposed: Industrial Control Systems' and 'ChatGPT for Cybersecurity Cookbook.' These works underscore his wide-ranging knowledge and expertise in cybersecurity, establishing him as a thought leader in this ever-evolving field. Denis Rothman: Expert in AI Transformers including ChatGPT/GPT-4, Bestselling Author John K. Thompson: Bestselling Author, Innovator in Data, AI, & Technology Amit Kumar: Sr. Enterprise Solutions Architect - Generative AI, NVIDIA Vinoo Ganesh: Speaker, Technologist, and Startup Advisor Amey Dharwadker: Engineering Leader, Machine Learning at Meta Bill Schmarzo: Dean of Big Data, Bestselling AI author Maria Parysz: AI Practitioner, CEO and Owner at LogicAI & RecoAI, ElephantAI Sadid Hasan: AI Lead, Microsoft Andreas Welsch: Chief AI Strategist, Intelligence Briefing Aleksander Molak: Causality Advocate, Bestselling Author, AI Researcher & Strategist Shyam Varan Nath: Specialist Leader - AI & Analytics, Deloitte Sebastian Raschka: Sebastian Raschka is an Assistant Professor of Statistics at the University of Wisconsin-Madison focusing on machine learning and deep learning research. As Lead AI Educator at Grid AI, Sebastian plans to continue following his passion for helping people get into machine learning and artificial intelligence.
    Note: Online resource; title from title details screen (O'Reilly, viewed Decenber 19, 2023)
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