R Deep Learning Cookbook

R Deep Learning Cookbook

By : PKS Prakash, Achyutuni Sri Krishna Rao

Buy this Book

R Deep Learning Cookbook

By: PKS Prakash, Achyutuni Sri Krishna Rao

Buy this Book

Overview of this book

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.

Title Page

Credits

About the Authors

About the Reviewer

www.PacktPub.com

Customer Feedback

Preface

Free Chapter

Getting Started

Introduction

Installing R with an IDE

Installing a Jupyter Notebook application

Starting with the basics of machine learning in R

Setting up deep learning tools/packages in R

Installing MXNet in R

Installing TensorFlow in R

Installing H2O in R

Installing all three packages at once using Docker

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

Convolution Neural Network

Introduction

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

Data Representation Using Autoencoders

Introduction

Setting up autoencoders

Data normalization

Setting up a regularized autoencoder

Fine-tuning the parameters of the autoencoder

Setting up stacked autoencoders

Setting up denoising autoencoders

Building and comparing stochastic encoders and decoders

Learning manifolds from autoencoders

Evaluating the sparse decomposition

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

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

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

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

Reinforcement Learning

Introduction

Setting up a Markov Decision Process

Performing model-based learning

Performing model-free learning

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

Application of Deep Learning to Signal processing

Introducing and preprocessing music MIDI files

Building an RBM model

Generating new music notes

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

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Evaluating the sparse decomposition

The sparse autoencoder is also known as over-complete representation and has a higher number of nodes in the hidden layer. The sparse autoencoders are usually executed with the sparsity parameter (regularization), which acts as a constraint and restricts the node to being active. The sparsity can also be assumed as nodes dropout caused due to sparsity constraints. The loss function for a sparse autoencoder consists of a reconstruction error, a regularization term to contain the weight decay, and KL divergence for sparsity constraint. The following representation gives a very good illustration of what we are talking about:

Getting ready

The dataset is loaded and set up.
Install and load the autoencoder package using the following script:

install.packages("autoencoder")
require(autoencoder)

How to do it...

The standard autoencoder code of TensorFlow can easily be extended to the sparse autoencoder module by updating the cost function. This section will introduce...

R Deep Learning Cookbook

By : PKS Prakash, Achyutuni Sri Krishna Rao

R Deep Learning Cookbook

By: PKS Prakash, Achyutuni Sri Krishna Rao

Overview of this book

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