Deep Learning with TensorFlow 2 and Keras: Regression, ConvNets, GANs, RNNs, NLP, and more with TensorFlow 2 and the Keras API

Book Description
Deep Learning with TensorFlow 2 and Keras, Second Edition teaches neural networks and deep learning techniques alongside TensorFlow (TF) and Keras. You’ll learn how to write deep learning applications in the most powerful, popular, and scalable machine learning stack available.

TensorFlow is the machine learning library of choice for professional applications, while Keras offers a simple and powerful Python API for accessing TensorFlow. TensorFlow 2 provides full Keras integration, making advanced machine learning easier and more convenient than ever before.

This book also introduces neural networks with TensorFlow, runs through the main applications (regression, ConvNets (CNNs), GANs, RNNs, NLP), covers two working example apps, and then dives into TF in production, TF mobile, and using TensorFlow with AutoML.

What you will learn
Build machine learning and deep learning systems with TensorFlow 2 and the Keras API
Use Regression analysis, the most popular approach to machine learning
Understand ConvNets (convolutional neural networks) and how they are essential for deep learning systems such as image classifiers
Use GANs (generative adversarial networks) to create new data that fits with existing patterns
Discover RNNs (recurrent neural networks) that can process sequences of input intelligently, using one part of a sequence to correctly interpret another
Apply deep learning to natural human language and interpret natural language texts to produce an appropriate response
Train your models on the cloud and put TF to work in real environments
Explore how Google tools can automate simple ML workflows without the need for complex modeling
Who this book is for
This book is for Python developers and data scientists who want to build machine learning and deep learning systems with TensorFlow. This book gives you the theory and practice required to use Keras, TensorFlow 2, and AutoML to build machine learning systems. Some knowledge of machine learning is expected.

Table of Contents
Neural Network Foundations with TensorFlow 2.0
TensorFlow 1.x and 2.x
Regression
Convolutional Neural Networks
Advanced Convolutional Neural Networks
Generative Adversarial Networks
Word Embeddings
Recurrent Neural Networks
Autoencoders
Unsupervised Learning
Reinforcement Learning
TensorFlow and Cloud
TensorFlow for Mobile and IoT and TensorFlow.js
An introduction to AutoML
The Math Behind Deep Learning
Tensor Processing Unit