《TensorFlow 1.x Deep Learning Cookbook: Over 90 unique recipes to solve artificial-intelligence driven problems with Python》是Packt Publishing出版的圖書，作者是Antonio Gulli，Amita Kapoor
基本介紹
- ISBN：9781788293594
- 作者：Antonio Gulli、Amita Kapoor
- 出版社：Packt Publishing
- 出版時間：2017年12月12日
- 頁數：486
- 定價：USD 37.40
- 裝幀：Paperback
內容簡介
Key Features Skill up and implement tricky neural networks using Google's TensorFlow 1.xAn 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 Book Description Deep neural networks (DNNs) have achieved a lot of success in the fiel...(展開全部) Key Features Skill up and implement tricky neural networks using Google's TensorFlow 1.xAn 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 Book Description 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 pl
