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R Deep Learning Projects: Master the techniques to design and develop neural network models in R

R Deep Learning Projects: Master the techniques to design and develop neural network models in R
English | 22 Feb. 2018 | ISBN: 1788478401 | 258 Pages | AZW3/MOBI/EPUB/PDF (conv) | 26.16 MB

5 real-world projects to help you master deep learning concepts

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

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.

What you will learn
Instrument Deep Learning models with packages such as deepnet, MXNetR, Tensorflow, H2O, Keras, and text2vec
Apply neural networks to perform handwritten digit recognition using MXNet
Get the knack of CNN models, Neural Network API, Keras, and TensorFlow for traffic sign classification
Implement credit card fraud detection with Autoencoders
Master reconstructing images using variational autoencoders
Wade through sentiment analysis from movie reviews
Run from past to future and vice versa with bidirectional Long Short-Term Memory (LSTM) networks
Understand the applications of Autoencoder Neural Networks in clustering and dimensionality reduction

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
Handwritten Digit Recognition using Convolutional Neural Networks
Traffic Signs Recognition for Intelligent Vehicles
Fraud Detection with Autoencoders
Text Generation using Recurrent Neural Networks
Sentiment Analysis with Word Embedding

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