R Deep Learning Projects (pdf)

$5.00

Author Yuxi (Hayden) Liu, Pablo Maldonado
Edition 1
Edition Year 2018
Format PDF
ISBN 9781788478403
Language English
Number Of Pages 260
Publisher Packt Publishing

Description

Who this book is for

Machine learning professionals and data scientists looking to master deep learning by implementing practical projects in R will find this book a useful resource. A knowledge of R programming and the basic concepts of deep learning is required to get the best out of this book.

Table of Contents

  1. Handwritten Digit Recognition using Convolutional Neural Networks
  2. Traffic Signs Recognition for Intelligent Vehicles
  3. Fraud Detection with Autoencoders
  4. Text Generation using Recurrent Neural Networks
  5. Sentiment Analysis with Word Embedding

Book Description

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.

Key Features

  • Master the different deep learning paradigms and build real-world projects related to text generation, sentiment analysis, fraud detection, and more
  • Get to grips with R’s impressive range of Deep Learning libraries and frameworks such as deepnet, MXNetR, Tensorflow, H2O, Keras, and text2vec
  • Practical projects that show you how to implement different neural networks with helpful tips, tricks, and best practices

Additional information

Author

Yuxi (Hayden) Liu, Pablo Maldonado

Edition

1

Edition Year

2018

Format

PDF

ISBN

9781788478403

Language

English

Number Of Pages

260

Publisher

Packt Publishing

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