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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

Deep Learning with TensorFlow 2 and Keras: Regression, ConvNets, GANs, RNNs, NLP, and more with TensorFlow 2 and the Keras API, 2nd Edition

Antonio Gulli
4.5/5 ( ratings)
Read Download
Build machine and deep learning systems with the newly released TensorFlow 2 and Keras for the lab, production, and mobile devices
Key Features
Introduces and then uses TensorFlow 2 and Keras right from the start
Teaches key machine and deep learning techniques
Understand the fundamentals of deep learning and machine learning through clear explanations and extensive code samples
Book Description Deep Learning with TensorFlow 2 and Keras, Second Edition teaches neural networks and deep learning techniques alongside TensorFlow 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 , 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 and how they are essential for deep learning systems such as image classifiers
Use GANs to create new data that fits with existing patterns
Discover RNNs 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
Who this book is for This book is for Python developers and data scientists who want to build machine learning and deep learning systems with TensorFlow. Whether or not you have done machine learning before, this book gives you the theory and practice required to use Keras, TensorFlow 2, and AutoML to build machine learning systems. Table of Contents
Neural Network Foundations with TensorFlow 2.0
TensorFlow 1.x and 2.x
Regression
Convolutional Neural Networks
Advanced Convolutional Neural Networks
Generative Adversarial Networks
Word Embeddings
Recurrent Neural Networks
Autoencoders
Unsupervised Learning
Reinforcement Learning
TensorFlow and Cloud
TensorFlow for Mobile and IoT and TensorFlow.js
An introduction to AutoML
The Math Behind Deep Learning
Tensor Processing Unit
Pages
646
Format
Kindle Edition
Publisher
Packt Publishing
Release
December 27, 2019

Deep Learning with TensorFlow 2 and Keras: Regression, ConvNets, GANs, RNNs, NLP, and more with TensorFlow 2 and the Keras API, 2nd Edition

Antonio Gulli
4.5/5 ( ratings)
Read Download
Build machine and deep learning systems with the newly released TensorFlow 2 and Keras for the lab, production, and mobile devices
Key Features
Introduces and then uses TensorFlow 2 and Keras right from the start
Teaches key machine and deep learning techniques
Understand the fundamentals of deep learning and machine learning through clear explanations and extensive code samples
Book Description Deep Learning with TensorFlow 2 and Keras, Second Edition teaches neural networks and deep learning techniques alongside TensorFlow 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 , 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 and how they are essential for deep learning systems such as image classifiers
Use GANs to create new data that fits with existing patterns
Discover RNNs 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
Who this book is for This book is for Python developers and data scientists who want to build machine learning and deep learning systems with TensorFlow. Whether or not you have done machine learning before, this book gives you the theory and practice required to use Keras, TensorFlow 2, and AutoML to build machine learning systems. Table of Contents
Neural Network Foundations with TensorFlow 2.0
TensorFlow 1.x and 2.x
Regression
Convolutional Neural Networks
Advanced Convolutional Neural Networks
Generative Adversarial Networks
Word Embeddings
Recurrent Neural Networks
Autoencoders
Unsupervised Learning
Reinforcement Learning
TensorFlow and Cloud
TensorFlow for Mobile and IoT and TensorFlow.js
An introduction to AutoML
The Math Behind Deep Learning
Tensor Processing Unit
Pages
646
Format
Kindle Edition
Publisher
Packt Publishing
Release
December 27, 2019

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