This book is for data scientists, machine learning practitioners, deep learning researchers, and AI enthusiasts who want to learn key tasks in the deep learning domain using a recipe-based approach. You will implement deep learning techniques and algorithms in common and not-so-common challenges faced in research work or projects. A strong understanding of machine learning and a working knowledge of R is mandatory.

#### Deep Learning with R Cookbook

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#### Deep Learning with R Cookbook

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#### 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

Understanding Neural Networks and Deep Neural Networks

Free Chapter

Working with Convolutional Neural Networks

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

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