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R Deep Learning Cookbook 英文原版.pdf
Cover
Copyright
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Customer Feedback
Table of Contents
Preface
Chapter 1: Getting Started
Introduction
Installing R with an IDE
Getting ready
How to do it...
Installing a Jupyter Notebook application
How to do it...
There's more...
Starting with the basics of machine learning in R
How to do it...
How it works...
Setting up deep learning tools/packages in R
How to do it...
Installing MXNet in R
Getting ready
How to do it...
Installing TensorFlow in R
Getting ready
How to do it...
How it works...
See also
Installing H2O in R
Getting ready
How to do it...
How it works...
There's more...
Installing all three packages at once using Docker
Getting ready
How to do it...
There's more...
Chapter 2: Deep Learning with R
Starting with logistic regression
Getting ready
How to do it...
Introducing the dataset
Getting ready
How to do it...
Performing logistic regression using H2O
Getting ready
How to do it...
How it works...
See also
Performing logistic regression using TensorFlow
Getting ready
How to do it...
How it works...
Visualizing TensorFlow graphs
Getting ready
How to do it...
How it works...
Starting with multilayer perceptrons
Getting ready
How to do it...
There's more...
See also
Setting up a neural network using H2O
Getting ready
How to do it...
How it works...
Tuning hyper-parameters using grid searches in H2O
Getting ready
How to do it...
How it works...
Setting up a neural network using MXNet
Getting ready
How to do it...
How it works...
Setting up a neural network using TensorFlow
Getting ready
How to do it...
How it works...
There's more...
Chapter 3: Convolution Neural Network
Introduction
Downloading and configuring an image dataset
Getting ready
How to do it...
How it works...
See also
Learning the architecture of a CNN classifier
Getting ready
How to do it...
How it works...
Using functions to initialize weights and biases
Getting ready
How to do it...
How it works...
Using functions to create a new convolution layer
Getting ready
How to do it...
How it works...
Using functions to create a new convolution layer
Getting ready
How to do it...
How it works...
Using functions to flatten the densely connected layer
Getting ready
How to do it...
How it works...
Defining placeholder variables
Getting ready
How to do it...
How it works...
Creating the first convolution layer
Getting ready
How to do it...
How it works...
Creating the second convolution layer
Getting ready
How to do it...
How it works...
Flattening the second convolution layer
Getting ready
How to do it...
How it works...
Creating the first fully connected layer
Getting ready
How to do it...
How it works...
Applying dropout to the first fully connected layer
Getting ready
How to do it...
How it works...
Creating the second fully connected layer with dropout
Getting ready
How to do it...
How it works...
Applying softmax activation to obtain a predicted class
Getting ready
How to do it...
Defining the cost function used for optimization
Getting ready
How to do it...
How it works...
Performing gradient descent cost optimization
Getting ready
How to do it...
Executing the graph in a TensorFlow session
Getting ready
How to do it...
How it works...
Evaluating the performance on test data
Getting ready
How to do it...
How it works...
Chapter 4: Data Representation Using Autoencoders
Introduction
Setting up autoencoders
Getting ready
How to do it...
Data normalization
Getting ready
Visualizing dataset distribution
How to do it...
How to set up an autoencoder model
Running optimization
Setting up a regularized autoencoder
Getting ready
How to do it...
How it works...
Fine-tuning the parameters of the autoencoder
Setting up stacked autoencoders
Getting ready
How to do it...
Setting up denoising autoencoders
Getting ready
How to do it...
Reading the dataset
Corrupting data to train
Setting up a denoising autoencoder
How it works...
Building and comparing stochastic encoders and decoders
Getting ready
How to do it...
Setting up a VAE model
Output from the VAE autoencoder
Learning manifolds from autoencoders
How to do it...
Setting up principal component analysis
Evaluating the sparse decomposition
Getting ready
How to do it...
How it works...
Chapter 5: Generative Models in Deep Learning
Comparing principal component analysis with the Restricted Boltzmann machine
Getting ready
How to do it...
Setting up a Restricted Boltzmann machine for Bernoulli distribution input
Getting ready
How to do it...
Training a Restricted Boltzmann machine
Getting ready
Example of a sampling
How to do it...
Backward or reconstruction phase of RBM
Getting ready
How to do it...
Understanding the contrastive divergence of the reconstruction
Getting ready
How to do it...
How it works...
Initializing and starting a new TensorFlow session
Getting ready
How to do it...
How it works...
Evaluating the output from an RBM
Getting ready
How to do it...
How it works...
Setting up a Restricted Boltzmann machine for Collaborative Filtering
Getting ready
How to do it...
Performing a full run of training an RBM
Getting ready
How to do it...
Setting up a Deep Belief Network
Getting ready
How to do it...
How it works...
Implementing a feed-forward backpropagation Neural Network
Getting ready
How to do it...
How it works...
Setting up a Deep Restricted Boltzmann Machine
Getting ready
How to do it...
How it works...
Chapter 6: Recurrent Neural Networks
Setting up a basic Recurrent Neural Network
Getting ready
How to do it...
How it works...
Setting up a bidirectional RNN model
Getting ready
How to do it...
Setting up a deep RNN model
How to do it...
Setting up a Long short-term memory based sequence model
How to do it...
How it works...
Chapter 7: Reinforcement Learning
Introduction
Setting up a Markov Decision Process
Getting ready
How to do it...
Performing model-based learning
How to do it...
Performing model-free learning
Getting ready
How to do it...
Chapter 8: Application of Deep Learning in Text Mining
Performing preprocessing of textual data and extraction of sentiments
How to do it...
How it works...
Analyzing documents using tf-idf
How to do it...
How it works...
Performing sentiment prediction using LSTM network
How to do it...
How it works...
Application using text2vec examples
How to do it...
How it works...
Chapter 9: Application of Deep Learning to Signal processing
Introducing and preprocessing music MIDI files
Getting ready
How to do it...
Building an RBM model
Getting ready
How to do it...
Generating new music notes
How to do it...
Chapter 10: Transfer Learning
Introduction
Illustrating the use of a pretrained model
Getting ready
How to do it...
Setting up the Transfer Learning model
Getting ready
How to do it...
Building an image classification model
Getting ready
How to do it...
Training a deep learning model on a GPU
Getting ready
How to do it...
Comparing performance using CPU and GPU
Getting ready
How to do it...
There's more...
See also
Index
Dr. PKS Prakash,
Achyutuni Sri Krishna Rao
R Deep
Learning
Cookbook
Solve complex neural net problems with TensorFlow,
H2O and MXNet
R Deep Learning Cookbook
Dr. PKS Prakash
Achyutuni Sri Krishna Rao
BIRMINGHAM - MUMBAI
R Deep Learning Cookbook
Copyright © 2017 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.
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warranty, either express or implied. Neither the authors, nor Packt Publishing, and its
dealers and distributors will be held liable for any damages caused or alleged to be caused
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However, Packt Publishing cannot guarantee the accuracy of this information.
First published: August 2017
Production reference: 1030817
Published by Packt Publishing Ltd.
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B3 2PB, UK.
ISBN 978-1-78712-108-9
Credits
Authors
Dr. PKS Prakash
Achyutuni Sri Krishna Rao
Copy Editor
Manisha Sinha
Reviewers
Vahid Mirjalili
Project Coordinator
Manthan Patel
Commissioning Editor
Veena Pagare
Proofreader
Safis Editing
Acquisition Editor
Aman Singh
Indexer
Tejal Daruwale Soni
Content Development Editor
Tejas Limkar
Graphics
Tania Dutta
Technical Editor
Sagar Sawant
Production Coordinator
Deepika Naik
About the Authors
Dr. PKS Prakash is a data scientist and an author. He has spent the last 12 years developing
many data science solutions to problems from leading companies in the healthcare,
manufacturing, pharmaceutical, and e-commerce domain. He is working as a Data Science
Manager at ZS Associates.
ZS is one of the world's largest business service firms, helping clients with commercial
success by creating data-driven strategies using advanced analytics, which they can
implement within their sales and marketing operations to make them more competitive,
and by helping them deliver impact where it matters.
Prakash obtained a PhD in Industrial and System Engineering from Wisconsin-Madison,
US. He defended his second PhD in Engineering from University of Warwick, UK. His
educational background also includes a master's degree from the University of Wisconsin-
Madison, US, and a bachelor's degree from National Institute of Foundry and Forge
Technology (NIFFT), India. He is the co-founder of Warwick Analytics, which is based on
his PhD work from the University of Warwick, UK.
Prakash is published widely in research areas of operational research and management, soft
computing tools, and advance algorithms in leading journals such as IEEE-Trans, EJOR, and
IJPR, among others. He has edited an issue of Intelligent Approaches to Complex Systems and
contributed to Evolutionary Computing in Advanced Manufacturing, published by Wiley, and
Algorithms and Data Structures Using R, published by Packt.
This book would not have been possible without the support and love from my wife, Dr.
Ritika Singh, and my daughter, Nishidha Singh. Also, I would like to extend my
special thanks to so many people from the Packt team whose names may not all be
mentioned, but their contribution is sincerely appreciated and gratefully acknowledged.
The book started with an early discussion with Aman Singh (Acquisition Editor), so I
want to extend special thanks to him as without his input, this book would have never
happened. Also, I want to thank Tejas Limkar (Content Development Editor) for
continuously pushing us and getting the book delivered on time. I would like to extend
thanks to all the reviewers, whose feedback has helped us tremendously in improving the
book.
Achyutuni Sri Krishna Rao is a data scientist, a civil engineer, and an author. He has spent
the last four years developing many data science solutions to problems from leading
companies in the healthcare, pharmaceuticals, and manufacturing. He is working as a Data
Science Consultant at ZS Associates.
Sri Krishna's background includes a master's degree in Enterprise Business Analytics and
Machine Learning from National University of Singapore, Singapore. His educational
background also includes a bachelor's degree from National Institute of Technology
Warangal, India.
Sri Krishna is published widely in research areas of civil engineering. He has contributed to
a book titled Algorithms and Data Structures Using R, published by Packt.
The journey of this book has been quite memorable, and I would like to give the credit to my
loving wife and my baby (on the way). I like to extend special thanks to my caring parents
and my adorable sister. Also, I would gratefully acknowledge the support from the entire
Packt team, especially Aman Singh (Acquisition Editor) and Tejas Limkar (Content
Development Editor), for striving to get the book delivered on time. I would like to extend
thanks to all the reviewers, whose feedback has helped us tremendously in improving the
book.
About the Reviewer
Vahid Mirjalili is a software engineer/data scientist, currently working toward his PhD in
Computer Science at Michigan State University. His research at the i-PRoBE (integrated
pattern recognition and biometrics) involves attribute classification of face images from
large image datasets. He also teaches Python programming as well as computing concepts
for data analysis and databases. With his specialty in data mining, he is very interested in
predictive modeling and getting insights from data. He is also a Python developer and likes
to contribute to the open source community. He enjoys making tutorials for different areas
of data science and computer algorithms, which can be found in his GitHub repository, at
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