In this book, we have tried to make a case for using deep learning within R. Most deep learning power-users would shun R and go for other languages, such as Python. We, however, believe that R has a solid ecosystem of packages, and visualization and manipulation tools, that can be combined with deep learning libraries to create interesting projects. R also has a huge base of users without a software engineering background (perhaps you are one of them?), and those users are increasingly interested in applications that deep learning is making possible, but they do not have time to learn Python.
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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