Hands-On Deep Learning with R by Michael Pawlus -- Read -- Imperial Library of Trantor

Index

Title Page Copyright and Credits

Hands-On Deep Learning with R

Dedication About Packt

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Contributors

About the authors About the reviewers Packt is searching for authors like you

Preface

Who this book is for What this book covers To get the most out of this book

Download the example code files Download the color images Conventions used

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Reviews

Section 1: Deep Learning Basics Machine Learning Basics

An overview of machine learning Preparing data for modeling

Handling missing values

Training a model on prepared data

Train and test data Choosing an algorithm

Evaluating model results

Machine learning metrics

Improving model results Reviewing different algorithms Summary

Setting Up R for Deep Learning

Technical requirements Installing the packages

Installing ReinforcementLearning Installing RBM Installing Keras Installing H2O Installing MXNet

Preparing a sample dataset Exploring Keras

Available functions A Keras example

Exploring MXNet

Available functions Getting started with MXNet

Exploring H2O

Available functions An H2O example

Exploring ReinforcementLearning and RBM

Reinforcement learning example An RBM example

Comparing the deep learning libraries Summary

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

Exploring the sigmoid function Investigating the hyperbolic tangent function Plotting the rectified linear units activation function Calculating the Leaky ReLU activation function Defining the swish activation function

Predicting class likelihood with softmax

Creating a feedforward network

Writing a neural network with Base R Creating a model with Wisconsin cancer data

Augmenting our neural network with backpropagation Summary

Section 2: Deep Learning Applications CNNs for Image Recognition

Technical requirements Image recognition with shallow nets Image recognition with convolutional neural networks

Optimizers Loss functions Evaluation metrics

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

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

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

Hyperparameter tuning Adding dropout layers Adjusting for user-item bias

Summary

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

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

Technical requirements An overview of GANs Defining the generator model Defining the discriminator model Preparing and preprocessing a dataset

Loading the libraries and data files Resizing our images Merging arrays

Training and evaluating the model

Defining the GAN model Passing data to the GAN model Training the GAN model

Generating random images Selecting real images Combining real and fake images Creating target labels Passing input to the discriminator model Updating the row selector Evaluating the model

Summary

Section 3: Reinforcement Learning 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

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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