Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data.
The updated edition of this practical book uses concrete examples, minimal theory, and three production-ready Python frameworks--scikit-learn, Keras, and TensorFlow--to help you gain an intuitive understanding of the concepts and tools for building intelligent systems. You'll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you've learned, all you need is programming experience to get started.
Описание: Learn how to solve challenging machine learning problems with TensorFlow, Google`s revolutionary new software library for deep learning. If you have some background in basic linear algebra and calculus, this practical book introduces machine-learning fundamentals.
Автор: Hope Tom, Resheff Yehezkel S., Lieder Itay Название: Learning Tensorflow: A Guide to Building Deep Learning Systems ISBN: 1491978511 ISBN-13(EAN): 9781491978511 Издательство: Wiley Рейтинг: Цена: 7602.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This practical book provides an end-to-end guide to TensorFlow, the leading open source software library that helps you build and train neural networks for computer vision, natural language processing (NLP), speech recognition, and general predictive analytics.
Автор: Gulli Antonio, Kapoor Amita Название: Tensorflow 1.X Deep Learning Cookbook ISBN: 1788293592 ISBN-13(EAN): 9781788293594 Издательство: Неизвестно Цена: 9010.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Take the next step in implementing various common and not-so-common neural networks with Tensorflow 1.x About This Book - Skill up and implement tricky neural networks using Google's TensorFlow 1.x - An easy-to-follow guide that lets you explore reinforcement learning, GANs, autoencoders, multilayer perceptrons and more. - Hands-on recipes to work with Tensorflow on desktop, mobile, and cloud environment Who This Book Is For This book is intended for data analysts, data scientists, machine learning practitioners and deep learning enthusiasts who want to perform deep learning tasks on a regular basis and are looking for a handy guide they can refer to. People who are slightly familiar with neural networks, and now want to gain expertise in working with different types of neural networks and datasets, will find this book quite useful. What You Will Learn - Install TensorFlow and use it for CPU and GPU operations - Implement DNNs and apply them to solve different AI-driven problems. - Leverage different data sets such as MNIST, CIFAR-10, and Youtube8m with TensorFlow and learn how to access and use them in your code. - Use TensorBoard to understand neural network architectures, optimize the learning process, and peek inside the neural network black box. - Use different regression techniques for prediction and classification problems - Build single and multilayer perceptrons in TensorFlow - Implement CNN and RNN in TensorFlow, and use it to solve real-world use cases. - Learn how restricted Boltzmann Machines can be used to recommend movies. - Understand the implementation of Autoencoders and deep belief networks, and use them for emotion detection. - Master the different reinforcement learning methods to implement game playing agents. - GANs and their implementation using TensorFlow. In Detail Deep neural networks (DNNs) have achieved a lot of success in the field of computer vision, speech recognition, and natural language processing. The entire world is filled with excitement about how deep networks are revolutionizing artificial intelligence. This exciting recipe-based guide will take you from the realm of DNN theory to implementing them practically to solve the real-life problems in artificial intelligence domain. In this book, you will learn how to efficiently use TensorFlow, Google's open source framework for deep learning. You will implement different deep learning networks such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Deep Q-learning Networks (DQNs), and Generative Adversarial Networks (GANs) with easy to follow independent recipes. You will learn how to make Keras as backend with TensorFlow. With a problem-solution approach, you will understand how to implement different deep neural architectures to carry out complex tasks at work. You will learn the performance of different DNNs on some popularly used data sets such as MNIST, CIFAR-10, Youtube8m, and more. You will not only learn about the different mobile and embedded platforms supported by TensorFlow but also how to set up cloud platforms for deep learning applications. Get a sneak peek of TPU architecture and how they will affect DNN future. By using crisp, no-nonsense recipes, you will become an expert in implementing deep learning techniques in growing real-world applications and research areas such as reinforcement learning, GANs, autoencoders and more. Style and approach This book consists of hands-on recipes where you'll deal with real-world problems. You'll execute a series of tasks as you walk through data mining challenges using TensorFlow 1.x. Your one-stop solution for common and not-so-common pain points, this is a book that you must have on the shelf.
Описание: This book is designed to guide you through TensorFlow 2 and how to use it effectively. Throughout the book, you will work through recipes and get hands-on experience to perform complex data computations, gain insights into your data, and more.
Описание: Working with AI is complicated and expensive for many developers. That`s why cloud providers have stepped in to make it easier, offering free (or affordable) state-of-the-art models and training tools. With this book, you`ll learn how to use Google`s AI-powered cloud services to do everything from creating a chatbot to analyzing text.
Описание: This book is a comprehensive introduction for those who are new to scalable and optimized TensorFlow for production. You will learn how to deliver enterprise-grade support for your existing and newly built AI applications. You will address the various needs of AI-enabled organizations to manage and scale machine learning workloads in production.
Описание: This volume reports on excavations in advance of the development of a site in Norton-on-Derwent, North Yorkshire close to the line of the main Roman road running from the crossing point of the River Derwent near Malton Roman fort to York. This site provided much additional information on aspects of the poorly understood `small town` of Delgovicia.
Автор: Boschetti Alberto, Massaron Luca, Thakur Abhishek Название: Tensorflow Deep Learning Projects ISBN: 1788398068 ISBN-13(EAN): 9781788398060 Издательство: Неизвестно Цена: 8091.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book is your guide to master deep learning with TensorFlow, with the help of 10 real-world projects. You will train high-performance models in TensorFlow to generate captions for images automatically, predict stocks` performance, create intelligent chatbots, perform large-scale text classification, develop recommendation systems, and more.
Neural networks are getting smaller. Much smaller. The OK Google team, for example, has run machine learning models that are just 14 kilobytes in size--small enough to work on the digital signal processor in an Android phone. With this practical book, you'll learn about TensorFlow Lite for Microcontrollers, a miniscule machine learning library that allows you to run machine learning algorithms on tiny hardware.
Authors Pete Warden and Daniel Situnayake explain how you can train models that are small enough to fit into any environment, including small embedded devices that can run for a year or more on a single coin cell battery. Ideal for software and hardware developers who want to build embedded devices using machine learning, this guide shows you how to create a TinyML project step-by-step. No machine learning or microcontroller experience is necessary.
Learn practical machine learning applications on embedded devices, including simple uses such as speech recognition and gesture detection
Train models such as speech, accelerometer, and image recognition, you can deploy on Arduino and other embedded platforms
Understand how to work with Arduino and ultralow-power microcontrollers
Use techniques for optimizing latency, energy usage, and model and binary size
Описание: This Workshop will teach you how to build deep learning models from scratch using real-world datasets with the TensorFlow framework. You will gain the knowledge you need to process a variety of data types, perform tensor computations, and understand the different layers in a deep learning model.
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