R Deep Learning Projects: Master the techniques to train and deploy neural networks in R

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

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 like 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 one of the popularly used languages 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 see 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 the neural networks can be trained using the capabilities of the GPU. 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 the 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 signs classification
• Implement Credit Card Fraud Detection with Autoencoders
• Be a Maestro in 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 to be a useful resource. Knowledge of R programming and the basic concepts of deep learning is required to get the best out of this book.