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Hands-On Automated Machine Learning: A beginner's guide to building automated machine learning systems using AutoML and Python

Automate data and model pipelines for faster machine learning applicationsKey FeaturesBuild automated modules for different machine learning componentsUnderstand each component of a machine learning pipeline in depthLearn to use different open source AutoML and feature engineering platformsBook DescriptionAutoML is designed to automate parts of Machine Learning. Readily available AutoML tools are making data science practitioners’ work easy and are received well in the advanced analytics community. Automated Machine Learning covers the necessary foundation needed to create automated machine learning modules and helps you get up to speed with them in the most practical way possible. In this book, you’ll learn how to automate different tasks in the machine learning pipeline such as data preprocessing, feature selection, model training, model optimization, and much more. In addition to this, it demonstrates how you can use the available automation libraries, such as auto-sklearn and MLBox, and create and extend your own custom AutoML components for Machine Learning. By the end of this book, you will have a clearer understanding of the different aspects of automated Machine Learning, and you’ll be able to incorporate automation tasks using practical datasets. You can leverage your learning from this book to implement Machine Learning in your projects and get a step closer to winning various machine learning competitions.What you will learnUnderstand the fundamentals of Automated Machine Learning systemsExplore auto-sklearn and MLBox for AutoML tasks Automate your preprocessing methods along with feature transformationEnhance feature selection and generation using the Python stackAssemble individual components of ML into a complete AutoML frameworkDemystify hyperparameter tuning to optimize your ML modelsDive into Machine Learning concepts such as neural networks and autoencoders Understand the information costs and trade-offs associated with AutoMLWho this book is forIf you’re a budding data scientist, data analyst, or Machine Learning enthusiast and are new to the concept of automated machine learning, this book is ideal for you. You’ll also find this book useful if you’re an ML engineer or data professional interested in developing quick machine learning pipelines for your projects. Prior exposure to Python programming will help you get the best out of this book.

Copyright: 2018

ISBN: 9781788622288

Features: Contains images

Hands-On Automation Testing with Java for Beginners: Build automation testing frameworks from scratch with Java

by Rahul Shetty

Learn Java programming concepts to design automation testing frameworksKey FeaturesLearn to use Java program logic in application testingUnderstand various test-driven development concepts with Java toolsMaster Java with lots of programming examplesBook DescriptionJava is one of the most commonly-used software languages by programmers and developers. Are you from a non-technical background and looking to master Java for your automation needs? Then Hands-On Automation Testing with Java for Beginners is for you.This book provides you with efficient techniques to effectively handle Java-related automation projects. You will learn how to handle strings and their functions in Java. As you make your way through the book, you will get to grips with classes and objects, along with their uses. In the concluding chapters, you will learn about the importance of inheritance and exceptions with practical examples. By the end of this book, you will have gained comprehensive knowledge of Java.What you will learnUnderstand the practical usage of Java conditions and loopsWrite any Java program logic with strategies, tips, and tricksLeverage advanced topics in Java collections to solve Java-related problemsUnderstand and use objects, classes, methods, and functions in JavaBuild Java automation frameworks from scratchObtain knowledge of Java object-oriented programming (OOP) concepts with practical implementationsWho this book is forHands-On Automation Testing with Java for Beginners is for software developers who want to step into the world of software quality assurance and perform automation testing using various testing frameworks. Prior experience of writing tests in Java is assumed.

Copyright: 2018

ISBN: 9781789539769

Features: Contains images

Hands-On Azure Digital Twins: A practical guide to building distributed IoT solutions

by Benjamin Planche Eliot Andres

A practical guide to building high performance systems for object detection, segmentation, video processing, smartphone applications, and more. This book is based on the alpha version of TensorFlow 2.Key FeaturesDiscover how to build, train, and serve your own deep neural networks with TensorFlow 2 and KerasApply modern solutions to a wide range of applications such as object detection and video analysisLearn how to run your models on mobile devices and webpages and improve their performanceBook DescriptionComputer vision solutions are becoming increasingly common, making their way in fields such as health, automobile, social media, and robotics. This book will help you explore TensorFlow 2, the brand new version of Google's open source framework for machine learning. You will understand how to benefit from using convolutional neural networks (CNNs) for visual tasks.Hands-On Computer Vision with TensorFlow 2 starts with the fundamentals of computer vision and deep learning, teaching you how to build a neural network from scratch. You will discover the features that have made TensorFlow the most widely used AI library, along with its intuitive Keras interface, and move on to building, training, and deploying CNNs efficiently. Complete with concrete code examples, the book demonstrates how to classify images with modern solutions, such as Inception and ResNet, and extract specific content using You Only Look Once (YOLO), Mask R-CNN, and U-Net. You will also build Generative Adversarial Networks (GANs) and Variational Auto-Encoders (VAEs) to create and edit images, and LSTMs to analyze videos. In the process, you will acquire advanced insights into transfer learning, data augmentation, domain adaptation, and mobile and web deployment, among other key concepts.By the end of the book, you will have both the theoretical understanding and practical skills to solve advanced computer vision problems with TensorFlow 2.0.What you will learnCreate your own neural networks from scratchClassify images with modern architectures including Inception and ResNetDetect and segment objects in images with YOLO, Mask R-CNN, and U-NetTackle problems in developing self-driving cars and facial emotion recognition systemsBoost your application’s performance with transfer learning, GANs, and domain adaptationUse recurrent neural networks for video analysisOptimize and deploy your networks on mobile devices and in the browserWho this book is forIf you’re new to deep learning and have some background in Python programming and image processing, like reading/writing image files and editing pixels, this book is for you. Even if you’re an expert curious about the new TensorFlow 2 features, you’ll find this book useful.While some theoretical explanations require knowledge in algebra and calculus, the book covers concrete examples for learners focused on practical applications such as visual recognition for self-driving cars and smartphone apps.

ISBN: 9781788839266

Features: Contains images

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