Deep Learning with TensorFlow and Keras: Build and deploy supervised, unsupervised, deep, and reinforcement

Build cutting edge machine and deep learning systems for the lab, production, and mobile devices.

Key Features

Understand the fundamentals of deep learning and machine learning through clear explanations and extensive code samples

Implement graph neural networks, transformers using Hugging Face and TensorFlow Hub, and joint and contrastive learning

Learn cutting-edge machine and deep learning techniques

Book Description

Deep Learning with TensorFlow and Keras teaches you neural networks and deep learning techniques using 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 2.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

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