Book Image

Deep Learning with TensorFlow - Second Edition

By : Giancarlo Zaccone, Md. Rezaul Karim
Book Image

Deep Learning with TensorFlow - Second Edition

By: Giancarlo Zaccone, Md. Rezaul Karim

Overview of this book

Deep learning is a branch of machine learning algorithms based on learning multiple levels of abstraction. Neural networks, which are at the core of deep learning, are being used in predictive analytics, computer vision, natural language processing, time series forecasting, and to perform a myriad of other complex tasks. This book is conceived for developers, data analysts, machine learning practitioners and deep learning enthusiasts who want to build powerful, robust, and accurate predictive models with the power of TensorFlow, combined with other open source Python libraries. Throughout the book, you’ll learn how to develop deep learning applications for machine learning systems using Feedforward Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Autoencoders, and Factorization Machines. Discover how to attain deep learning programming on GPU in a distributed way. You'll come away with an in-depth knowledge of machine learning techniques and the skills to apply them to real-world projects.

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Table of Contents (15 chapters)
Deep Learning with TensorFlow - Second Edition
Deep Learning with TensorFlow - Second Edition
Contributors
Contributors
Preface
Preface
Other Books You May Enjoy
Other Books You May Enjoy
Free Chapter
1
Getting Started with Deep Learning
Getting Started with Deep Learning
A soft introduction to machine learning
Artificial neural networks
How does an ANN learn?
Neural network architectures
Deep learning frameworks
Summary
2
A First Look at TensorFlow
A First Look at TensorFlow
A general overview of TensorFlow
What's new from TensorFlow v1.6 forwards?
Installing and configuring TensorFlow
TensorFlow computational graph
TensorFlow code structure
Data model in TensorFlow
Visualizing computations through TensorBoard
Linear regression and beyond
Summary
3
Feed-Forward Neural Networks with TensorFlow
Feed-Forward Neural Networks with TensorFlow
Feed-forward neural networks (FFNNs)
Implementing a feed-forward neural network
Implementing a multilayer perceptron (MLP)
Tuning hyperparameters and advanced FFNNs
Summary
4
Convolutional Neural Networks
Convolutional Neural Networks
Main concepts of CNNs
CNNs in action
LeNet5
Implementing a LeNet-5 step by step
Dataset preparation
Fine-tuning implementation
Inception-v3
Emotion recognition with CNNs
Summary
5
Optimizing TensorFlow Autoencoders
Optimizing TensorFlow Autoencoders
How does an autoencoder work?
Implementing autoencoders with TensorFlow
Improving autoencoder robustness
Fraud analytics with autoencoders
Summary
6
Recurrent Neural Networks
Recurrent Neural Networks
Working principles of RNNs
RNN and the gradient vanishing-exploding problem
Implementing an RNN for spam prediction
Developing a predictive model for time series data
An LSTM predictive model for sentiment analysis
Human activity recognition using LSTM model
Summary
7
Heterogeneous and Distributed Computing
Heterogeneous and Distributed Computing
GPGPU computing
The TensorFlow GPU setup
Distributed computing
The distributed TensorFlow setup
Summary
8
Advanced TensorFlow Programming
Advanced TensorFlow Programming
tf.estimator
TFLearn
PrettyTensor
Keras
Summary
9
Recommendation Systems Using Factorization Machines
Recommendation Systems Using Factorization Machines
Recommendation systems
Movie recommendation using collaborative filtering
Factorization machines for recommendation systems
Improved factorization machines
Summary
10
Reinforcement Learning
Reinforcement Learning
The RL problem
OpenAI Gym
The Q-Learning algorithm
Deep Q-learning
Summary
Index
Index

Deep Learning with TensorFlow - Second Edition

Copyright © 2018 Packt Publishing

All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.

Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the authors, nor Packt Publishing or its dealers and distributors, will be held liable for any damages caused or alleged to have been caused directly or indirectly by this book.

Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.

Acquisition Editors: Ben Renow-Clarke, Suresh Jain

Project Editor: Savvy Sequeira

Content Development Editors: Jo Lovell

Technical Editor: Nidhisha Shetty

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Indexers: Tejal Daruwale Soni

Graphics: Tom Scaria

Production Coordinator: Arvindkumar Gupta

First published: April 2017

Second edition: March 2018

Production reference: 1290318

Published by Packt Publishing Ltd.

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Birmingham B3 2PB, UK.

ISBN 978-1-78883-110-9

www.packtpub.com

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