R Deep Learning Projects
R is a popular programming language used by statisticians and mathematicians for statistical analysis, and is popularly used for deep learning. 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 see how to train effective neural networks in R—including convolutional neural networks, recurrent neural networks and LSTMs—and also see 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.
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Difficulty Level
Intermediate
Completion Time
8h36m
Language
English
About Book
Who Is This Book 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.
R Deep Learning Projects
- About Book
- Who Is This Book For?
- Book Content
Book content
chapters • 8h36m total length
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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