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  • MPI Ethno. Forsch.  (11)
  • Soni, Mitesh  (6)
  • Bonaccorso, Giuseppe  (5)
  • Birmingham, UK : Packt Publishing  (11)
  • 1
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    Hands-on unsupervised learning with Python : implement machine learning and deep learning models using Scikit-Learn, TensorFlow, and more (2019)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781789349276 , 1789349273
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Python (Computer program language) ; Artificial intelligence ; Machine learning ; Electronic books ; Electronic books ; local
    Abstract: Discover the skill-sets required to implement various approaches to Machine Learning with Python Key Features Explore unsupervised learning with clustering, autoencoders, restricted Boltzmann machines, and more Build your own neural network models using modern Python libraries Practical examples show you how to implement different machine learning and deep learning techniques Book Description Unsupervised learning is about making use of raw, untagged data and applying learning algorithms to it to help a machine predict its outcome. With this book, you will explore the concept of unsupervised learning to cluster large sets of data and analyze them repeatedly until the desired outcome is found using Python. This book starts with the key differences between supervised, unsupervised, and semi-supervised learning. You will be introduced to the best-used libraries and frameworks from the Python ecosystem and address unsupervised learning in both the machine learning and deep learning domains. You will explore various algorithms, techniques that are used to implement unsupervised learning in real-world use cases. You will learn a variety of unsupervised learning approaches, including randomized optimization, clustering, feature selection and transformation, and information theory. You will get hands-on experience with how neural networks can be employed in unsupervised scenarios. You will also explore the steps involved in building and training a GAN in order to process images. By the end of this book, you will have learned the art of unsupervised learning for different real-world challenges. What you will learn Use cluster algorithms to identify and optimize natural groups of data Explore advanced non-linear and hierarchical clustering in action Soft label assignments for fuzzy c-means and Gaussian mixture models Detect anomalies through density estimation Perform principal component analysis using neural network models Create unsupervised models using GANs Who this book is for This book is intended for statisticians, data scientists, machine learning developers, and deep learning practitioners who want to build smart applications by implementing key building block unsupervised learning, and master all the new techniques and algorithms offered in machine learning and deep learning using real-world examples. Some prior knowledge of machine learning concepts and statistics is desirable.
    Note: Includes bibliographical references. - Description based on online resource; title from title page (Safari, viewed April 18, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781789348279/?ar
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  • 2
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    Machine learning algorithms : popular algorithms for data science and machine learning (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781789345483 , 1789345480
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Edition: Second edition.
    Keywords: Machine learning ; Computer algorithms ; Electronic books ; Electronic books ; local
    Abstract: An easy-to-follow, step-by-step guide for getting to grips with the real-world application of machine learning algorithms Key Features Explore statistics and complex mathematics for data-intensive applications Discover new developments in EM algorithm, PCA, and bayesian regression Study patterns and make predictions across various datasets Book Description Machine learning has gained tremendous popularity for its powerful and fast predictions with large datasets. However, the true forces behind its powerful output are the complex algorithms involving substantial statistical analysis that churn large datasets and generate substantial insight. This second edition of Machine Learning Algorithms walks you through prominent development outcomes that have taken place relating to machine learning algorithms, which constitute major contributions to the machine learning process and help you to strengthen and master statistical interpretation across the areas of supervised, semi-supervised, and reinforcement learning. Once the core concepts of an algorithm have been covered, you'll explore real-world examples based on the most diffused libraries, such as scikit-learn, NLTK, TensorFlow, and Keras. You will discover new topics such as principal component analysis (PCA), independent component analysis (ICA), Bayesian regression, discriminant analysis, advanced clustering, and gaussian mixture. By the end of this book, you will have studied machine learning algorithms and be able to put them into production to make your machine learning applications more innovative. What you will learn Study feature selection and the feature engineering process Assess performance and error trade-offs for linear regression Build a data model and understand how it works by using different types of algorithm Learn to tune the parameters of Support Vector Machines (SVM) Explore the concept of natural language processing (NLP) and recommendation systems Create a machine learning architecture from scratch Who this book is for Machine Learning Algorithms is for you if you are a machine learning engineer, data engineer, or junior data scientist who wants to advance in the field of predictive analytics and machine learning. Familiarity with R and Python will be an added advantage for getting the best from this book. Downloading the example code for this book You can download the example code files for all Packt books you have purchased from your account at http://www.PacktPub.com. I...
    Note: Previous edition published: 2017. - Description based on online resource; title from title page (Safari, viewed October 2, 2018)
    URL: https://learning.oreilly.com/library/view/-/9781789347999/?ar
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  • 3
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    Python advanced guide to artificial intelligence : expert machine learning systems and intelligent agents using Python (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781789951721 , 1789951720
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Series Statement: Learning path
    Keywords: Python (Computer program language) ; Artificial intelligence ; Machine learning ; Electronic books ; Electronic books ; local
    Abstract: Demystify the complexity of machine learning techniques and create evolving, clever solutions to solve your problems Key Features Master supervised, unsupervised, and semi-supervised ML algorithms and their implementation Build deep learning models for object detection, image classification, similarity learning, and more Build, deploy, and scale end-to-end deep neural network models in a production environment Book Description This Learning Path is your complete guide to quickly getting to grips with popular machine learning algorithms. You'll be introduced to the most widely used algorithms in supervised, unsupervised, and semi-supervised machine learning, and learn how to use them in the best possible manner. Ranging from Bayesian models to the MCMC algorithm to Hidden Markov models, this Learning Path will teach you how to extract features from your dataset and perform dimensionality reduction by making use of Python-based libraries. You'll bring the use of TensorFlow and Keras to build deep learning models, using concepts such as transfer learning, generative adversarial networks, and deep reinforcement learning. Next, you'll learn the advanced features of TensorFlow1.x, such as distributed TensorFlow with TF clusters, deploy production models with TensorFlow Serving. You'll implement different techniques related to object classification, object detection, image segmentation, and more. By the end of this Learning Path, you'll have obtained in-depth knowledge of TensorFlow, making you the go-to person for solving artificial intelligence problems This Learning Path includes content from the following Packt products: Mastering Machine Learning Algorithms by Giuseppe Bonaccorso Mastering TensorFlow 1.x by Armando Fandango Deep Learning for Computer Vision by Rajalingappaa Shanmugamani What you will learn Explore how an ML model can be trained, optimized, and evaluated Work with Autoencoders and Generative Adversarial Networks Explore the most important Reinforcement Learning techniques Build end-to-end deep learning (CNN, RNN, and Autoencoders) models Who this book is for This Learning Path is for data scientists, machine learning engineers, artificial intelligence engineers who want to delve into complex machine learning algorithms, calibrate models, and improve the predictions of the trained model. You will encounter the advanced intricacies and complex use cases of deep learning and AI. A basic knowledge of programming in Python and some un...
    Note: Description based on online resource; title from cover (Safari, viewed February 22, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781789957211/?ar
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  • 4
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    Mastering machine learning algorithms : expert techniques to implement popular machine learning algorithms and fine-tune your models (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781788625906 , 1788625900
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Machine learning ; Computer algorithms ; Electronic books ; Electronic books ; local
    Abstract: Explore and master the most important algorithms for solving complex machine learning problems. About This Book Discover high-performing machine learning algorithms and understand how they work in depth. One-stop solution to mastering supervised, unsupervised, and semi-supervised machine learning algorithms and their implementation. Master concepts related to algorithm tuning, parameter optimization, and more Who This Book Is For This book is an ideal and relevant source of content for data science professionals who want to delve into complex machine learning algorithms, calibrate models, and improve the predictions of the trained model. A basic knowledge of machine learning is preferred to get the best out of this guide. What You Will Learn Explore how a ML model can be trained, optimized, and evaluated Understand how to create and learn static and dynamic probabilistic models Successfully cluster high-dimensional data and evaluate model accuracy Discover how artificial neural networks work and how to train, optimize, and validate them Work with Autoencoders and Generative Adversarial Networks Apply label spreading and propagation to large datasets Explore the most important Reinforcement Learning techniques In Detail Machine learning is a subset of AI that aims to make modern-day computer systems smarter and more intelligent. The real power of machine learning resides in its algorithms, which make even the most difficult things capable of being handled by machines. However, with the advancement in the technology and requirements of data, machines will have to be smarter than they are today to meet the overwhelming data needs; mastering these algorithms and using them optimally is the need of the hour. Mastering Machine Learning Algorithms is your complete guide to quickly getting to grips with popular machine learning algorithms. You will be introduced to the most widely used algorithms in supervised, unsupervised, and semi-supervised machine learning, and will learn how to use them in the best possible manner. Ranging from Bayesian models to the MCMC algorithm to Hidden Markov models, this book will teach you how to extract features from your dataset and perform dimensionality reduction by making use of Python-based libraries such as scikit-learn. You will also learn how to use Keras and TensorFlow to train effective neural networks. If you are looking for a single resource to study, implement, and solve end-to-end machine learning problems...
    Note: Description based on online resource; title from title page (Safari, viewed June 29, 2018)
    URL: https://learning.oreilly.com/library/view/-/9781788621113/?ar
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  • 5
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    Practical AWS networking : build and manage complex networks using services such as Amazon VPC, Elastic Load Balancing, Direct Connect, and Amazon Route 53 (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781788479028 , 1788479025
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Amazon Web Services (Firm) ; Web applications ; Development ; Application software ; Development ; Cloud computing ; Electronic books ; Electronic books ; local
    Abstract: Your one step guide to learn all about AWS networking. About This Book Master your networking skills on Public Cloud. Gain hands-on experience of using Amazon VPC, Elastic Load Balancing, Direct Connect and other AWS products. Implement troubleshooting skills and best practices for security on AWS network. Who This Book Is For This book is targeted towards cloud architects, cloud solution providers, or any stakeholders dealing with networking on AWS Cloud. A prior idea of Amazon Web Services will be an added advantage. What You Will Learn Overview of all networking services available in AWS. Gain Work with load balance application across different regions. Learn auto scale instance based on the increase and decrease of the traffic. Deploy application in highly available and fault tolerant manner. Configure Route 53 for a web application. Troubleshooting tips and best practices at the end In Detail Amazon Web Services (AWS) dominates the public cloud market by a huge margin and it continues to be the first choice for many organizations. Networking has been an area of focus for all the leading cloud service providers. AWS has a suite of network-related products that help to perform network-related task in AWS. This book initially covers the basics of networking in AWS. Then we use AWS VPC to create an isolated virtual cloud for performing network-related tasks. We then provide an overview of AWS Direct Connect after taking a deep dive into scalability and load balancing using Auto scaling feature, Elastic Load Balancing, and Amazon Route S3. Toward the end of the book, we cover some troubleshooting tips and security best practices for your network. By the end of this book, you will have hands-on experience of working with network tasks on AWS. Style and approach A step by step practical guide that helps you use all networking services available in AWS effectively.
    Note: Description based on online resource; title from title page (Safari, viewed February 1, 2018)
    URL: https://learning.oreilly.com/library/view/-/9781788398299/?ar
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  • 6
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    Machine learning algorithms : reference guide for popular algorithms for data science and machine learning (2017)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781785884511 , 1785884514
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Machine learning ; Computer algorithms ; Electronic books ; Electronic books ; local
    Abstract: Build strong foundation for entering the world of Machine Learning and data science with the help of this comprehensive guide About This Book Get started in the field of Machine Learning with the help of this solid, concept-rich, yet highly practical guide. Your one-stop solution for everything that matters in mastering the whats and whys of Machine Learning algorithms and their implementation. Get a solid foundation for your entry into Machine Learning by strengthening your roots (algorithms) with this comprehensive guide. Who This Book Is For This book is for IT professionals who want to enter the field of data science and are very new to Machine Learning. Familiarity with languages such as R and Python will be invaluable here. What You Will Learn Acquaint yourself with important elements of Machine Learning Understand the feature selection and feature engineering process Assess performance and error trade-offs for Linear Regression Build a data model and understand how it works by using different types of algorithm Learn to tune the parameters of Support Vector machines Implement clusters to a dataset Explore the concept of Natural Processing Language and Recommendation Systems Create a ML architecture from scratch. In Detail As the amount of data continues to grow at an almost incomprehensible rate, being able to understand and process data is becoming a key differentiator for competitive organizations. Machine learning applications are everywhere, from self-driving cars, spam detection, document search, and trading strategies, to speech recognition. This makes machine learning well-suited to the present-day era of Big Data and Data Science. The main challenge is how to transform data into actionable knowledge. In this book you will learn all the important Machine Learning algorithms that are commonly used in the field of data science. These algorithms can be used for supervised as well as unsupervised learning, reinforcement learning, and semi-supervised learning. A few famous algorithms that are covered in this book are Linear regression, Logistic Regression, SVM, Naive Bayes, K-Means, Random Forest, TensorFlow, and Feature engineering. In this book you will also learn how these algorithms work and their practical implementation to resolve your problems. This book will also introduce you to the Natural Processing Language and Recommendation systems, which help you run multiple algorithms simultaneously. On completion of the book you will...
    Note: Description based on online resource; title from title page (Safari, viewed August 17, 2017)
    URL: https://learning.oreilly.com/library/view/-/9781785889622/?ar
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  • 7
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    Implementing DevOps with Microsoft Azure : leverage Visual Studio Team Services to automate Microsoft Azure deployments and incorporate the DevOps culture (2017)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781787128125 , 1787128121
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Windows Azure ; Application software ; Development ; Information technology ; Management ; Cloud computing ; Electronic books ; Electronic books ; local
    Abstract: Accelerate and Automate Build, Deploy, and Management of applications to achieve High Availability. About This Book This guide highlights tools that offer development and deployment environments for application services Secure and continuously monitor your web application in order to make it highly available Use Visual Studio Team Services for Continuous Integration and Continuous Development to expedite your application life cycle management process Use Microsoft Azure App Services (Azure Web Apps / Azure Websites), PaaS offering from Microsoft to deploy web application Who This Book Is For This book is for DevOps engineers, system administrators, and developers (.net) who want to implement DevOps for their organization. You do not need to have any knowledge of VSTS or Azure App Services (Azure Web Apps / Azure Websites). What You Will Learn Explore the features of PaaS and aPaaS in DevOps Use Visual Studio Team Services (VSTS) to manage versions of code and integrating VSTS with Eclipse IDE Understand and configure Continuous Integration in VSTS Review Unit Test Execution for Automated Testing Create different environments that can be used to continuous deploy a web application Configure Roll-based Access to enable secure access for Azure Web Apps Create and configure the App Service Environment to enhance security Understand the execution of the end-to-end automation process Conduct Performance Testing using JMeter Discover the different monitoring options available in Microsoft Azure Portal In Detail This book will teach you all about the Visual Studio Team Services and Microsoft Azure PaaS offerings that support Continuous Integration, Continuous Delivery, Continuous Deployment, and execution in the cloud with high availability, disaster recovery, and security. You will first be given a tour of all the concepts and tools that Microsoft Azure has to offer and how these can be used in situations to cultivate the DevOps culture. You'll be taught how to use and manage Visual Studio Team Services (VSTS) and about the structure of the sample application used throughout the book. You will become familiar with the nitty gritty of Continuous Integration and Continuous Development with VSTS and Microsoft Azure Apps. You will not only learn how to create App service environments, but also how to compare Azure Web Apps and App Service Environments to deploy web applications in a more secure environment. Once you have completed Continuous Integration ...
    Note: Description based on online resource; title from title page (Safari, viewed June 12, 2017)
    URL: https://learning.oreilly.com/library/view/-/9781787127029/?ar
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  • 8
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    Learning Azure Functions : build scalable cloud systems with serverless architecture (2017)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781787288997 , 1787288994
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Windows Azure ; Cloud computing ; Web services ; Application software ; Design ; Electronic books ; Electronic books ; local
    Abstract: Embracing the cloud - a Serverless architecture to solve problems at scale About This Book Learn to develop, manage, deploy, and monitor Azure functions in any language. Make the most out of Azure functions to build scalable systems. A step-by-step guide that will help you eliminate the pain points of implementing a serverless architecture. Who This Book Is For This book aims at IT architects and developers who want to build scalable systems and deploy serverless applications with Azure functions. No prior knowledge of Azure functions is necessary. What You Will Learn Understand the folder structure of a function and the purposes of the files Deploy a function and test it Explore the common triggers that are used to activate a function Discover how bindings can be used to output the results of a function Build a dll that has functionality that can be leveraged by a function Chain functions to allow the invocation of one function from another Understand how to monitor the health of your functions In Detail Functions help you easily run small pieces of code in cloud with Azure functions without worrying about a whole application or the infrastructure to run it. With Azure functions, you can use triggers to execute your code and bindings to simplify the input and output of your code. This book will start with the basics of Azure Functions. You will learn the steps to set up the environment and the tools that we will be using in the further chapters. Once you have a better understanding of this, we will be creating our first hello world function app. Later you will be introduced to triggers, how they are used to activate a function, and how binding can be used to output results of a function.You will also explore the steps to create an assembly with complex functionality that can be used by functions. Next, this book will teach you to scale your functions and use them to process data, integrate systems, and build simple APIs and microservices. Finally, this book will cover some diagnostic techniques with Azure App services and best practices of working with Azure Functions. By the end of this book, you will be well-versed with the techniques of scaling your Azure functions and making the most of serverless architecture. Style and Approach A step-by-side guide filled with real world examples that will guide you with the steps to build a scalable Cloud system
    Note: Description based on online resource; title from title page (Safari, viewed October 31, 2017)
    URL: https://learning.oreilly.com/library/view/-/9781787122932/?ar
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  • 9
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    DevOps bootcamp : a fast-paced guide to implement DevOps with ease (2017)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781787282230 , 1787282236
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Computer software ; Development ; Application software ; Development ; Web site development ; Electronic books ; Electronic books ; local
    Abstract: Sharpen your DevOps knowledge with DevOps Bootcamp About This Book Improve your organization's performance to ensure smooth production of software and services. Learn how Continuous Integration and Continuous Delivery practices can be utilized to cultivate the DevOps culture. A fast-paced guide filled with illustrations and best practices to help you consistently ship quality software. Who This Book Is For The book is aimed at IT Developers and Operations-administrators who want to quickly learn and implement the DevOps culture in their organization. What You Will Learn Static Code Analysis using SOnarqube Configure a Maven-based JEE Web Application Perform Continuous Integration using Jenkins and VSTS Install and configure Docker Converge a Chef node using a Chef workstation Accomplish Continuous Delivery in Microsoft Azure VM and Microsoft Azure App Services (Azure Web Apps) using Jenkins Perform Load Testing using Apache JMeter Build and Release Automation using Visual Studio Team Services Monitor Cloud-based resources In Detail DevOps Bootcamp delivers practical learning modules in manageable chunks. Each chunk is delivered in a day, and each day is a productive one. Each day builds your competency in DevOps. You will be able to take the task you learn every day and apply it to cultivate the DevOps culture. Each chapter presents core concepts and key takeaways about a topic in DevOps and provides a series of hands-on exercises. You will not only learn the importance of basic concepts or practices of DevOps but also how to use different tools to automate application lifecycle management. We will start off by building the foundation of the DevOps concepts. On day two, we will perform Continuous Integration using Jenkins and VSTS both by configuring Maven-based JEE Web Application​. We will also integrate Jenkins and Sonar qube for Static Code Analysis. Further, on day three, we will focus on Docker containers where we will install and configure Docker and also create a Tomcat Container to deploy our Java based web application. On day four, we will create and configure the environment for application deployment in AWS and Microsoft Azure Cloud for which we will use Infrastructure as a Service and Open Source Configuration Management tool Chef. For day five, our focus would be on Continuous Delivery. We will automate application deployment in Docker container using Jenkins Plugin, AWS EC2 using Script, AWS Elastic Beanstalk using Jenkins Plugi...
    Note: Description based on online resource; title from title page (Safari, viewed June 21, 2017)
    URL: https://learning.oreilly.com/library/view/-/9781787285965/?ar
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  • 10
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    Jenkins 2.x continuous integration cookbook : over 90 recipes to produce great results using pro-level practices, techniques, and solutions (2017)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781788292610 , 1788292618
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Edition: Third edition
    Parallel Title: Erscheint auch als
    Keywords: Computer software Quality control ; Computer software Testing ; Computer software Reliability
    Note: Description based on online resource; title from title page (Safari, viewed November 30, 2017). - Previous edition published: 2015
    URL: Volltext  (URL des Erstveröffentlichers)
    URL: https://learning.oreilly.com/library/view/-/9781788297943/?ar
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  • 11
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    DevOps for web development (2016)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781786468352 , 1786468352
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Computer software ; Development ; Application software ; Development ; Web site development ; Electronic books ; Electronic books ; local
    Abstract: Achieve the Continuous Integration and Continuous Delivery of your web applications with ease About This Book Overcome the challenges of implementing DevOps for web applications, familiarize yourself with diverse third-party modules, and learn how to integrate them with bespoke code to efficiently complete tasks Understand how to deploy web applications for a variety of Cloud platforms such as Amazon EC2, AWS Elastic Beanstalk, Microsoft Azure, Azure Web Apps, and Docker Container Understand how to monitor applications deployed in Amazon EC2, AWS Elastic Beanstalk, Microsoft Azure, Azure Web Apps using Nagios, New Relic, Microsoft Azure, and AWS default monitoring features Who This Book Is For If you are a system admin or application and web application developer with a basic knowledge of programming and want to get hands-on with tools such as Jenkins 2 and Chef, and Cloud platforms such as AWS and Microsoft Azure, Docker, New Relic, Nagios, and their modules to host, deploy, monitor, and manage their web applications, then this book is for you. What You Will Learn Grasp Continuous Integration for a JEE application - create and configure a build job for a Java application with Maven and with Jenkins 2.0 Create built-in delivery pipelines of Jenkins 2 and build a pipeline configuration for end-to-end automation to manage the lifecycle of Continuous Integration Get to know all about configuration management using Chef to create a runtime environment Perform instance provisioning in AWS and Microsoft Azure and manage virtual machines on different cloud platforms - install Knife plugins for Amazon EC2 and Microsoft Azure Deploy an application in Amazon EC2, AWS Elastic Beanstalk, Microsoft Azure Web Apps, and a Docker container Monitor infrastructure, application servers, web servers, and applications with the use of open source monitoring solutions and New Relic Orchestrate multiple build jobs to achieve application deployment automation - create parameterized build jobs for end-to-end automation In Detail The DevOps culture is growing at a massive rate, as many organizations are adopting it. However, implementing it for web applications is one of the biggest challenges experienced by many developers and admins, which this book will help you overcome using various tools, such as Chef, Docker, and Jenkins. On the basis of the functionality of these tools, the book is divided into three parts. The first part shows you how to use Jenkins 2.0 for Con...
    Note: Description based on online resource; title from cover (Safari, viewed November 4, 2016)
    URL: https://learning.oreilly.com/library/view/-/9781786465702/?ar
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