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Hands-On Deep Learning with R

By : Rodger Devine, Pawlus

Hands-On Deep Learning with R

By: Rodger Devine, Pawlus

Overview of this book

Deep learning enables efficient and accurate learning from a massive amount of data. This book will help you overcome a number of challenges using various deep learning algorithms and architectures with R programming. This book starts with a brief overview of machine learning and deep learning and how to build your first neural network. You’ll understand the architecture of various deep learning algorithms and their applicable fields, learn how to build deep learning models, optimize hyperparameters, and evaluate model performance. Various deep learning applications in image processing, natural language processing (NLP), recommendation systems, and predictive analytics will also be covered. Later chapters will show you how to tackle recognition problems such as image recognition and signal detection, programmatically summarize documents, conduct topic modeling, and forecast stock market prices. Toward the end of the book, you will learn the common applications of GANs and how to build a face generation model using them. Finally, you’ll get to grips with using reinforcement learning and deep reinforcement learning to solve various real-world problems. By the end of this deep learning book, you will be able to build and deploy your own deep learning applications using appropriate frameworks and algorithms.

Preface

Preface

Who this book is for

What this book covers

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Section 1: Deep Learning Basics

Section 1: Deep Learning Basics

Free Chapter

Machine Learning Basics

Machine Learning Basics

An overview of machine learning

Preparing data for modeling

Training a model on prepared data

Evaluating model results

Improving model results

Reviewing different algorithms

Summary

Setting Up R for Deep Learning

Setting Up R for Deep Learning

Technical requirements

Installing the packages

Preparing a sample dataset

Exploring Keras

Exploring MXNet

Exploring H2O

Exploring ReinforcementLearning and RBM

Comparing the deep learning libraries

Summary

Artificial Neural Networks

Artificial Neural Networks

Technical requirements

Contrasting deep learning with machine learning

Comparing neural networks and the human brain

Utilizing bias and activation functions within hidden layers

Surveying activation functions

Creating a feedforward network

Augmenting our neural network with backpropagation

Summary

Section 2: Deep Learning Applications

Section 2: Deep Learning Applications

CNNs for Image Recognition

CNNs for Image Recognition

Technical requirements

Image recognition with shallow nets

Image recognition with convolutional neural networks

Enhancing the model with additional layers

Choosing the most appropriate activation function

Selecting optimal epochs using dropout and early stopping

Summary

Multilayer Perceptron for Signal Detection

Multilayer Perceptron for Signal Detection

Technical requirements

Understanding multilayer perceptrons

Preparing and preprocessing data

Deciding on the hidden layers and neurons

Training and evaluating the model

Summary

Neural Collaborative Filtering Using Embeddings

Neural Collaborative Filtering Using Embeddings

Technical requirements

Introducing recommender systems

Collaborative filtering with neural networks

Exploring embeddings

Preparing, preprocessing, and exploring data

Performing exploratory data analysis

Creating user and item embeddings

Building and training a neural recommender system

Evaluating results and tuning hyperparameters

Summary

Deep Learning for Natural Language Processing

Deep Learning for Natural Language Processing

Formatting data using tokenization

Cleaning text to remove noise

Applying word embeddings to increase usable data

Clustering data into topic groups

Summarizing documents using model results

Creating an RBM

Defining the Gibbs sampling rate

Speeding up sampling with contrastive divergence

Computing free energy for model evaluation

Stacking RBMs to create a deep belief network

Summary

Long Short-Term Memory Networks for Stock Forecasting

Long Short-Term Memory Networks for Stock Forecasting

Technical requirements

Understanding common methods for stock market prediction

Preparing and preprocessing data

Configuring a data generator

Training and evaluating the model

Tuning hyperparameters to improve performance

Summary

Generative Adversarial Networks for Faces

Generative Adversarial Networks for Faces

Technical requirements

An overview of GANs

Defining the generator model

Defining the discriminator model

Preparing and preprocessing a dataset

Training and evaluating the model

Summary

Section 3: Reinforcement Learning

Section 3: Reinforcement Learning

Reinforcement Learning for Gaming

Reinforcement Learning for Gaming

Technical requirements

Understanding the concept of reinforcement learning

Preparing and processing data

Configuring the reinforcement agent

Tuning hyperparameters

Summary

Deep Q-Learning for Maze Solving

Deep Q-Learning for Maze Solving

Technical requirements

Creating an environment for reinforcement learning

Defining an agent to perform actions

Building a deep Q-learning model

Running the experiment

Improving performance with policy functions

Summary

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Setting Up R for Deep Learning

In this book, we will primarily use the following libraries for deep learning: H2O, MXNet, and Keras. We will also use the Restricted Boltzmann Machine (RBM) package specifically for RBMs and deep belief networks (DBNs). In addition, we will conclude the book by using the ReinforcementLearning package.

In this chapter, we will install all of the previously listed packages. Each package can be used to train deep learning models in R. However, each has its particular strengths and weaknesses. We will explore the underlying architecture for each of these packages, which will help us to understand how they execute code. The packages have been created to allow R programmers to perform deep learning, with the exception of RBM and ReinforcementLearning, which are not written natively in R. This does have important implications for us to consider...

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