Deep Learning with R for Beginners

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Title Page

Copyright and Credits

Deep Learning with R for Beginners

About Packt

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Contributors

About the authors

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Preface

Who this book is for

What this book covers

To get the most out of this book

Download the example code files

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Conventions used

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Reviews

Getting Started with Deep Learning

What is deep learning?

A conceptual overview of neural networks

Neural networks as an extension of linear regression

Neural networks as a network of memory cells

Deep neural networks

Some common myths about deep learning

Setting up your R environment

Deep learning frameworks for R

MXNet

Keras

Do I need a GPU (and what is it anyway)?

Setting up reproducible results

Summary

Training a Prediction Model

Neural networks in R

Building neural network models

Generating predictions from a neural network

The problem of overfitting data – the consequences explained

Use case – building and applying a neural network

Summary

Deep Learning Fundamentals

Building neural networks from scratch in R

Neural network web application

Neural network code

Back to deep learning

The symbol X y and ctx parameters

The num.round and begin.round parameters

The optimizer parameter

The initializer parameter

The eval.metric and eval.data parameters

The epoch.end.callback parameter

The array.batch.size parameter

Using regularization to overcome overfitting

L1 penalty

L1 penalty in action

L2 penalty

L2 penalty in action

Weight decay (L2 penalty in neural networks)

Ensembles and model-averaging

Use case – improving out-of-sample model performance using dropout

Summary

Training Deep Prediction Models

Getting started with deep feedforward neural networks

Activation functions

Introduction to the MXNet deep learning library

Deep learning layers

Building a deep learning model

Use case – using MXNet for classification and regression

Data download and exploration

Preparing the data for our models

The binary classification model

The regression model

Improving the binary classification model

The unreasonable effectiveness of data

Summary

Image Classification Using Convolutional Neural Networks

CNNs

Convolutional layers

Pooling layers

Dropout

Flatten layers dense layers and softmax

Image classification using the MXNet library

Base model (no convolutional layers)

LeNet

Classification using the fashion MNIST dataset

References/further reading

Summary

Tuning and Optimizing Models

Evaluation metrics and evaluating performance

Types of evaluation metric

Evaluating performance

Data preparation

Different data distributions

Data partition between training test and validation sets

Standardization

Data leakage

Data augmentation

Using data augmentation to increase the training data

Test time augmentation

Using data augmentation in deep learning libraries