Book Image

Deep Learning with R Cookbook

By : Swarna Gupta, Rehan Ali Ansari, Dipayan Sarkar
Book Image

Deep Learning with R Cookbook

By: Swarna Gupta, Rehan Ali Ansari, Dipayan Sarkar

Overview of this book

Deep learning (DL) has evolved in recent years with developments such as generative adversarial networks (GANs), variational autoencoders (VAEs), and deep reinforcement learning. This book will get you up and running with R 3.5.x to help you implement DL techniques. The book starts with the various DL techniques that you can implement in your apps. A unique set of recipes will help you solve binomial and multinomial classification problems, and perform regression and hyperparameter optimization. To help you gain hands-on experience of concepts, the book features recipes for implementing convolutional neural networks (CNNs), recurrent neural networks (RNNs), and Long short-term memory (LSTMs) networks, as well as sequence-to-sequence models and reinforcement learning. You’ll then learn about high-performance computation using GPUs, along with learning about parallel computation capabilities in R. Later, you’ll explore libraries, such as MXNet, that are designed for GPU computing and state-of-the-art DL. Finally, you’ll discover how to solve different problems in NLP, object detection, and action identification, before understanding how to use pre-trained models in DL apps. By the end of this book, you’ll have comprehensive knowledge of DL and DL packages, and be able to develop effective solutions for different DL problems.
Table of Contents (11 chapters)

Preface

Deep learning has taken a huge step in recent years with developments including generative adversarial networks (GANs), variational autoencoders, and deep reinforcement learning. This book serves as a reference guide in R 3.x that will help you implement deep learning techniques.

This book walks you through various deep learning techniques that you can implement in your applications using R 3.x. A unique set of recipes will help you solve regression, binomial classification, and multinomial classification problems, and explores hyper-parameter optimization in detail. You will also go through recipes that implement convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTM) networks, sequence-to-sequence models, GANs, and reinforcement learning. You will learn about high-performance computation involving large datasets that utilize GPUs, along with parallel computation capabilities in R, and you will also get familiar with libraries such as MXNet, which is designed for efficient GPU computing and state-of-the-art deep learning. You will also learn how to solve common and not-so-common problems in NLP, such as object detection and action identification, and you will leverage pre-trained models in deep learning applications.

By the end of the book, you will have a logical understanding of deep learning and different deep learning packages and will be able to build the most appropriate solutions to your problems.