Deep Learning with R Cookbook: Over 45 unique recipes to delve into neural network techniques using R 3.5.x

Book Description
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.

What you will learn
Work with different datasets for image classification using CNNs
Apply transfer learning to solve complex computer vision problems
Use RNNs and their variants such as LSTMs and Gated Recurrent Units (GRUs) for sequence data generation and classification
Implement autoencoders for DL tasks such as dimensionality reduction, denoising, and image colorization
Build deep generative models to create photorealistic images using GANs and VAEs
Use MXNet to accelerate the training of DL models through distributed computing
Who this book is for
This deep learning book is for data scientists, machine learning practitioners, deep learning researchers and AI enthusiasts who want to learn key tasks in deep learning domains using a recipe-based approach. A strong understanding of machine learning and working knowledge of the R programming language is mandatory.

Table of Contents
Understanding Neural Networks and Deep Neural Networks
Working with Convolutional Neural Network
Recurrent Neural Networks in Action
Implementing Autoencoders with Keras
Deep Generative Models
Handling Big Data Using Large-Scale Deep Learning
Working with Text and Audio for NLP
Deep Learning for Computer Vision
Implementing Reinforcement Learning