Sujit Pal
Deep Learning with TensorFlow and Keras: Build and deploy supervised, unsupervised, deep, and reinforcement learning models, 3rd Edition

x focuses on simplicity and ease of use, with updates like eager execution, intuitive higher-level APIs based on Keras, and flexible model building on any platform.

This book uses the latest TF 2.0 features and libraries to present an overview of supervised and unsupervised machine learning models and provides a comprehensive analysis of deep learning and reinforcement learning models using practical examples for the cloud, mobile, and large production environments.
This book also shows you how to create neural networks with TensorFlow, runs through popular algorithms (regression, convolutional neural networks (CNNs), transformers, generative adversarial networks (GANs), recurrent neural networks (RNNs), natural language processing (NLP), and graph neural networks (GNNs)), covers working example apps, and then dives into TF in production, TF mobile, and TensorFlow with AutoML.

What you will learn Learn how to use the popular GNNs with TensorFlow to carry out graph mining tasks Discover the world of transformers, from pretraining to fine-tuning to evaluating them Apply self-supervised learning to natural language processing, computer vision, and audio signal processing Combine probabilistic and deep learning models using TensorFlow Probability Train your models on the cloud and put TF to work in real environments Build machine learning and deep learning systems with TensorFlow 2.x and the Keras API Who this book is for
This hands-on machine learning 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, and AutoML to build machine learning systems.
Some machine learning knowledge would be useful.

We don't assume TF knowledge.

Table of Contents Neural Networks Foundations with TF Regression and Classification Convolutional Neural Networks Word Embeddings Recurrent Neural Network Transformers Unsupervised Learning Autoencoders Generative Models Self-Supervised Learning Reinforcement Learning Probabilistic TensorFlow An Introduction to AutoML The Math Behind Deep Learning Tensor Processing Unit Other Useful Deep Learning Libraries Graph Neural Networks Machine Learning Best Practices TensorFlow 2 Ecosystem Advanced Convolutional Neural Networks