Deep Learning with TensorFlow and Keras

Amita Kapoor, Antonio Gulli, Sujit Pal, Francois Chollet

• Publisher: Packt Publishing
• Year: 2022
• eBook ISBN: 9781803245713
• Edition: 3
• Topic: Informatica
• Subtopic: Informatica generale

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 techniquesBook DescriptionDeep 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 APIWho this book is forThis 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

• Preface
• Neural Network Foundations with TF
• Regression and Classification
• Convolutional Neural Networks
• Word Embeddings
• Recurrent Neural Networks
• 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
• Other Books You May Enjoy
• Index