Amazon Web Services (AWS) offers scalable, reliable, and easy to use on-demand cloud computing platforms and APIs on a pay-as-you-go basis. It is a comprehensive platform that provides access to numerous services such as computing, security services, analytics, database, storage, developer tools, and many other Infrastructures as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS) offerings. With this wide range of services available to individuals, profit and non-profit enterprises, and educational institutions, AWS is considered one of the most successful cloud infrastructure companies, holding the majority of the market share. In this section, we will primarily work with EC2, which is a virtual computing environment. We will also use an Amazon Machine Image (AMI) for deep learning using R. AMI has software...
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.
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Deep Learning with R Cookbook
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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