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)

Introduction to convolutional operations

The generic architecture of CNN is comprised of convolutional layers followed by fully connected layers. Like other neural networks, a CNN also contains input, hidden and output layers, but it works by restructuring the data into tensors that consist of the image, and the width and height of the image. In CNN, each volume in one layer is connected only to a spatially relevant region in the next layer to ensure that when the number of layers increases, each neuron has a local influence on its specific location. A CNN may also contain pooling layers along with few fully connected layers.

The following is an example of a simple CNN with convolution and pooling layers. In this recipe, we will work with convolution layers. We will introduce the concept of pooling layers in the Getting familiar with pooling layers recipe of...