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

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

Purchase of the print or Kindle book includes a free eBook in PDF format.

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

1. Neural Networks Foundations with TF
2. Regression and Classification
3. Convolutional Neural Networks
4. Word Embeddings
5. Recurrent Neural Network
6. Transformers
7. Unsupervised Learning
8. Autoencoders
9. Generative Models
10. Self-Supervised Learning
11. Reinforcement Learning
12. Probabilistic TensorFlow
13. An Introduction to AutoML
14. The Math Behind Deep Learning
15. Tensor Processing Unit
16. Other Useful Deep Learning Libraries
17. Graph Neural Networks
18. Machine Learning Best Practices
19. TensorFlow 2 Ecosystem
20. Advanced Convolutional Neural Networks

Amita Kapoor taught and supervised research in neural networks and artificial intelligence for 20+ years as Associate Professor in SRCASW, University of Delhi. She now provides her expertise in AI and EduTech to various organizations and companies.

Antonio Gulli has a passion for establishing and managing global technological talent for innovation and execution. His core expertise is in cloud computing, deep learning, and search engines. Currently, Antonio works for Google in the Cloud Office of the CTO in Zurich, working on Search, Cloud Infra, Sovereignty, and Conversational AI.

Sujit Pal is a Technology Research Director at Elsevier Labs, an advanced technology group within the Reed-Elsevier Group of companies. His interests include semantic search, natural language processing, machine learning, and deep learning. At Elsevier, he has worked on several initiatives involving search quality measurement and improvement, image classification and duplicate detection, and annotation and ontology development for medical and scientific corpora.