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Table Of Contents

R Deep Learning Projects

4 (5)

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R Deep Learning Projects

4 (5)

Buy this Book

Overview of this book

R is a popular programming language used by statisticians and mathematicians for statistical analysis, and is popularly used for deep learning. Deep Learning, as we all know, is one of the trending topics today, and is finding practical applications in a lot of domains. This book demonstrates end-to-end implementations of five real-world projects on popular topics in deep learning such as handwritten digit recognition, traffic light detection, fraud detection, text generation, and sentiment analysis. You'll learn how to train effective neural networks in R—including convolutional neural networks, recurrent neural networks, and LSTMs—and apply them in practical scenarios. The book also highlights how neural networks can be trained using GPU capabilities. You will use popular R libraries and packages—such as MXNetR, H2O, deepnet, and more—to implement the projects. By the end of this book, you will have a better understanding of deep learning concepts and techniques and how to use them in a practical setting.

Preface

Who this book is for

What this book covers

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Free Chapter

Handwritten Digit Recognition Using Convolutional Neural Networks

What is deep learning and why do we need it?

Handwritten digit recognition using CNNs

Summary

Traffic Sign Recognition for Intelligent Vehicles

How is deep learning applied in self-driving cars?

Traffic sign recognition using CNN

Dealing with a small training set – data augmentation

Reviewing methods to prevent overfitting in CNNs

Summary

Fraud Detection with Autoencoders

Getting ready

Our first examples

Credit card fraud detection with autoencoders

Variational Autoencoders

Text fraud detection

Summary

Text Generation Using Recurrent Neural Networks

What is so exciting about recurrent neural networks?

RNNs from scratch in R

RNN using Keras

Summary

Sentiment Analysis with Word Embeddings

Warm-up – data exploration

Bag of words benchmark

Word embeddings

Sentiment analysis from movie reviews

Mining sentiment from Twitter

Summary

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Sentiment Analysis with Word Embeddings

In this chapter, we turn to the problem of sentiment analysis. Sentiment analysis is an umbrella term for a number of techniques to figure out how a speaker feels about a certain topic or piece of content.

A vanilla case study of sentiment analysis is polarity. Given a document or text string (for instance, a Tweet, a review, or a comment on a social network), the aim is to determine whether the author feels good, bad, or neutral about the item or topic in question.

At first look, this problem might seem trivial: A lookup table with positive and negative words, and simply counting the word frequencies should do, right? Not so fast. Here are a few examples of why this is tricky: