R deep learning Cookbook : solve complex neural net problems with TensorFlow, H2O and MXNet / Dr. PKS Prakash, Achyutuni Sri Krishna Rao.

Format:

Book

Author/Creator:

Prakash, PKS, Dr., author.

Rao, Achyutuni Sri Krishna, author.

Language:

English

Subjects (All):

R (Computer program language).

Artificial intelligence.

Neural networks (Computer science).

Physical Description:

1 online resource (282 pages) : illustrations

Edition:

1st edition

Place of Publication:

Birmingham, [England] ; Mumbai, [India] : Packt Publishing, 2017.

System Details:

text file

Biography/History:

Prakash PKS: Dr. PKS Prakash is a data scientist and author. He has spent the last 12 years in developing many data science solutions in several practical areas in healthcare, manufacturing, pharmaceuticals, and e-commerce. He currently works as the data science manager at ZS Associates. He is the co-founder of Warwick Analytics, a spin-off from University of Warwick, UK. Prakash has published articles widely in research areas of operational research and management, soft computing tools, and advanced algorithms in leading journals such as IEEE-Trans, EJOR, and IJPR, among others. He has edited an article on Intelligent Approaches to Complex Systems and contributed to books such as Evolutionary Computing in Advanced Manufacturing published by WILEY and Algorithms and Data Structures using R and R Deep Learning Cookbook, published by PACKT. Sri Krishna Rao Achyutuni: Achyutuni Sri Krishna Rao is a Data Scientist, a Civil Engineer and an Author. He has spent last 4 years in developing many data science solutions to solve problems from leading companies in healthcare, pharmaceutical and manufacturing domain. He is working as Data Science Consultant at ZS Associates. Sri Krishnas background involves a masters in Enterprise Business Analytics and Machine Learning from National University of Singapore, Singapore. His other educational background involves a Bachelors from National Institute of Technology Warangal, India. Sri Krishna has published widely in research areas of civil engineering. He has contributed in a book titled Algorithms and Data Structures using R published by PACKT.

Summary:

Powerful, independent recipes to build deep learning models in different application areas using R libraries About This Book Master intricacies of R deep learning packages such as mxnet & tensorflow Learn application on deep learning in different domains using practical examples from text, image and speech Guide to set-up deep learning models using CPU and GPU Who This Book Is For Data science professionals or analysts who have performed machine learning tasks and now want to explore deep learning and want a quick reference that could address the pain points while implementing deep learning. Those who wish to have an edge over other deep learning professionals will find this book quite useful. What You Will Learn Build deep learning models in different application areas using TensorFlow, H2O, and MXnet. Analyzing a Deep boltzmann machine Setting up and Analysing Deep belief networks Building supervised model using various machine learning algorithms Set up variants of basic convolution function Represent data using Autoencoders. Explore generative models available in Deep Learning. Discover sequence modeling using Recurrent nets Learn fundamentals of Reinforcement Leaning Learn the steps involved in applying Deep Learning in text mining Explore application of deep learning in signal processing Utilize Transfer learning for utilizing pre-trained model Train a deep learning model on a GPU In Detail Deep Learning is the next big thing. It is a part of machine learning. It's favorable results in applications with huge and complex data is remarkable. Simultaneously, R programming language is very popular amongst the data miners and statisticians. This book will help you to get through the problems that you face during the execution of different tasks and Understand hacks in deep learning, neural networks, and advanced machine learning techniques. It will also take you through complex deep learning algorithms and various deep learning packages and libraries in R. It will be starting with different packages in Deep Learning to neural networks and structures. You will also encounter the applications in text mining and processing along with a comparison between CPU and GPU performance. By the end of the book, you will have a logical understanding of Deep learning and different deep learning packages to have the most appropriate solutions for your problems. Style and approach Collection of hands-on recipes that would act as your all-time reference for y...

Contents:

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

There's more...

Starting with the basics of machine learning in R

How it works...

Setting up deep learning tools/packages in R

Installing MXNet in R

Installing TensorFlow in R

See also

Installing H2O in R

Installing all three packages at once using Docker

Chapter 2: Deep Learning with R

Starting with logistic regression

Introducing the dataset

Performing logistic regression using H2O

Performing logistic regression using TensorFlow

Visualizing TensorFlow graphs

Starting with multilayer perceptrons

Setting up a neural network using H2O

Tuning hyper-parameters using grid searches in H2O

Setting up a neural network using MXNet

Setting up a neural network using TensorFlow

There's more.

Chapter 3: Convolution Neural Network

Downloading and configuring an image dataset

Learning the architecture of a CNN classifier

Using functions to initialize weights and biases

Using functions to create a new convolution layer

Using functions to flatten the densely connected layer

Defining placeholder variables

Creating the first convolution layer

Creating the second convolution layer

Flattening the second convolution layer

Creating the first fully connected layer

Applying dropout to the first fully connected layer

Creating the second fully connected layer with dropout

Applying softmax activation to obtain a predicted class

Defining the cost function used for optimization

Performing gradient descent cost optimization

Executing the graph in a TensorFlow session

Evaluating the performance on test data

How to do it.

How it works...

Chapter 4: Data Representation Using Autoencoders

Setting up autoencoders

Data normalization

Visualizing dataset distribution

How to set up an autoencoder model

Running optimization

Setting up a regularized autoencoder

Fine-tuning the parameters of the autoencoder

Setting up stacked autoencoders

Setting up denoising autoencoders

Reading the dataset

Corrupting data to train

Setting up a denoising autoencoder

Building and comparing stochastic encoders and decoders

Setting up a VAE model

Output from the VAE autoencoder

Learning manifolds from autoencoders

Setting up principal component analysis

Evaluating the sparse decomposition

Chapter 5: Generative Models in Deep Learning

Comparing principal component analysis with the Restricted Boltzmann machine

Setting up a Restricted Boltzmann machine for Bernoulli distribution input

Training a Restricted Boltzmann machine

Example of a sampling

Backward or reconstruction phase of RBM

Understanding the contrastive divergence of the reconstruction

Initializing and starting a new TensorFlow session

Evaluating the output from an RBM

How it works.

Setting up a Restricted Boltzmann machine for Collaborative Filtering

Performing a full run of training an RBM

Setting up a Deep Belief Network

Implementing a feed-forward backpropagation Neural Network

Setting up a Deep Restricted Boltzmann Machine

Chapter 6: Recurrent Neural Networks

Setting up a basic Recurrent Neural Network

Setting up a bidirectional RNN model

Setting up a deep RNN model

Setting up a Long short-term memory based sequence model

Chapter 7: Reinforcement Learning

Setting up a Markov Decision Process

Performing model-based learning

Performing model-free learning

Chapter 8: Application of Deep Learning in Text Mining

Performing preprocessing of textual data and extraction of sentiments

Analyzing documents using tf-idf

Performing sentiment prediction using LSTM network

Application using text2vec examples

Chapter 9: Application of Deep Learning to Signal processing

Introducing and preprocessing music MIDI files

Building an RBM model

Generating new music notes

Chapter 10: Transfer Learning

Introduction.

Illustrating the use of a pretrained model

Setting up the Transfer Learning model

Building an image classification model

Training a deep learning model on a GPU

Comparing performance using CPU and GPU

Index.

Notes:

Includes bibliographical references.

Description based on online resource; title from PDF title page (ebrary, viewed August 29, 2017).

ISBN:

9781523112616

1523112611

OCLC:

1001514453