Book Image

Hands-On Deep Learning with R

By : Michael Pawlus, Rodger Devine
Book Image

Hands-On Deep Learning with R

By: Michael Pawlus, Rodger Devine

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.

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Table of Contents (16 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
1
Section 1: Deep Learning Basics
Section 1: Deep Learning Basics
Free Chapter
2
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
3
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
4
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
5
Section 2: Deep Learning Applications
Section 2: Deep Learning Applications
6
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
7
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
8
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
9
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
10
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
11
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
12
Section 3: Reinforcement Learning
Section 3: Reinforcement Learning
13
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
14
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
15
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Computing free energy for model evaluation

RBMs belong to a class of energy-based models. These use a free energy equation that is analogous to the cost function in other machine learning algorithms. Just like a cost function, the objective is to minimize the free energy values. A lower free energy value equates to a higher probability that the visible unit variables are being described by the hidden units and a higher value equates to a lower likelihood. 

Let's now look at the three models we just created and compare free energy values for these models. We compare the free energy to identify which model is performing better by running the following code:

rbm5$e[1:10]
rbm3$e[1:10]
rbm1$e[1:10]

After running this code, an output similar to the following will be printed to your console:

In this case, using just one round of Gibbs sampling produces the best performing model...

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