Deep Learning with TensorFlow and Keras: Build and deploy supervised, unsupervised, deep, and reinforcement learning models, 3rd Edition 3rd ed. Edition
by Amita Kapoor ，Antonio Gulli ，Sujit Pal，Francois Chollet (Foreword)
Publisher finelybook 出版社: Packt Publishing; 3rd ed. edition (October 6, 2022)
Language 语言: English
Print Length 页数: 698 pages
ISBN-10: 1803232919
ISBN-13: 9781803232911
Book Description
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
