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 hands-on recipes that would act as your all-time reference for your deep learning needs
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 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 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
- Reinforcement Learning
- Application of Deep Learning in Text Mining
- Application of Deep Learning to Signal processing
- Transfer Learning