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)

Understanding strides and padding

In this recipe, we will learn about two key configuration hyperparameters of CNN, which are strides and padding. Strides are used mainly to reduce the size of the output volume. Padding is another technique that lets us preserve the dimensions of the input volume in the output volume, thus enabling us to extract the low-level features efficiently.

Strides: Stride, in very simple terms, means the step of the convolution operation. Stride specifies the amount by which filters convolve around the input. For example, if we specify the value of stride argument as 1, that means the filter will shift one unit at a time over the input matrix. 

Strides can be used for multiple purposes, primarily the following:

  • To avoid feature overlapping
  • To achieve smaller spatial dimensionality of the output volume

In the following diagram, you...