This book is meant mainly for two types of readers: professionals who use R in their daily work and want to know some of the tricks and best practices that we have discovered in our projects, and anyone who wants to jump into the field of deep learning with concrete use cases in mind. This is not a tutorial in R or machine learning; it is rather a real-world showcase of how deep learning can be used in industry, with examples borrowed from our own experience.

#### R Deep Learning Projects

#### R Deep Learning Projects

#### 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.

Table of Contents (7 chapters)

Preface

Free Chapter

Handwritten Digit Recognition Using Convolutional Neural Networks

Traffic Sign Recognition for Intelligent Vehicles

Fraud Detection with Autoencoders

Text Generation Using Recurrent Neural Networks

Sentiment Analysis with Word Embeddings

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