Book description
Powerful, independent recipes to build deep learning models in different application areas using R libraries
About This Book
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
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 handson recipes that would act as your alltime reference for your deep learning needs
Publisher resources
Table of contents
 Preface
 Getting Started

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 hyperparameters 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 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
 Finetuning 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 feedforward backpropagation Neural Network
 Setting up a Deep Restricted Boltzmann Machine
 Recurrent Neural Networks
 Reinforcement Learning
 Application of Deep Learning in Text Mining
 Application of Deep Learning to Signal processing
 Transfer Learning
Product information
 Title: R Deep Learning Cookbook
 Author(s):
 Release date: August 2017
 Publisher(s): Packt Publishing
 ISBN: 9781787121089
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