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Deep Learning with TensorFlow 2 and Keras: Regression,ConvNets,GANs,RNNs,NLP,and more with TensorFlow 2 and the Keras API,2nd Edition
Authors: Antonio Gulli - Amita Kapoor - Sujit Pal
ISBN-10 书号: 1838823417
ISBN-13 书号: 9781838823412
Publisher Finelybook 出版日期: 2019-12-27
pages 页数: 646 pages
Publisher Finelybook 出版社: Packt
Book Description
Build machine and deep learning systems with the newly released TensorFlow 2 and Keras for the lab,production,and mobile devices
Deep Learning with TensorFlow 2 and Keras,Second Edition teaches neural networks and deep learning techniques alongside 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 is the machine learning library of choice for professional applications,while Keras offers a simple and powerful Python API for accessing TensorFlow. TensorFlow 2 provides full Keras integration,making advanced machine learning easier and more convenient than ever before.
This book also introduces neural networks with TensorFlow,runs through the main applications (regression,ConvNets (CNNs),GANs,RNNs,NLP),covers two working example apps,and then dives into TF in production,TF mobile,and using TensorFlow with AutoML.
What you will learn
Build machine learning and deep learning systems with TensorFlow 2 and the Keras API
Use Regression analysis,the most popular approach to machine learning
Understand ConvNets (convolutional neural networks) and how they are essential for deep learning systems such as image classifiers
Use GANs (generative adversarial networks) to create new data that fits with existing patterns
Discover RNNs (recurrent neural networks) that can process sequences of input intelligently,using one part of a sequence to correctly interpret another
Apply deep learning to natural human language and interpret natural language texts to produce an appropriate response
Train your models on the cloud and put TF to work in real environments
Explore how Google tools can automate simple ML workflows without the need for complex modeling
Contents
Preface
Chapter 1: Neural Network Foundations with TensorFlow 2.0
Chapter 2: TensorFlow 1.x and 2.x
Chapter 3: Regression
Chapter 4: Convolutional Neural Networks
Chapter 5: Advanced Convolutional Neural Networks
Chapter 6: Generative Adversarial Networks
Chapter 7: Word Embeddings
Chapter 8: Recurrent Neural Networks
Chapter 9: Autoencoders
Chapter 10: Unsupervised Learning
Chapter 11: Reinforcement Learning
Chapter 12: TensorFlow and Cloud
Chapter 13: TensorFlow for Mobile and loT and TensorFlow.js
Chapter 14: An introduction to AutoML
Chapter 15: The Math Behind Deep Learning
Chapter 16: Tensor Processing Unit
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