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)

Speech recognition

In the past few decades, there has been a tremendous amount of research on leveraging deep learning for speech-related applications. Speech recognition has become a part of many day-to-day applications, such as our phones, smartwatches, homes, games, and many more.

It's being implemented as a salient feature in many voice search applications such as Siri and Alexa by tech giants such as Apple and Amazon, respectively. Sound waves are time-domain signals, which means that when we plot a sound wave, one of the axes is time (independent variable) and the other is the amplitude of the wave (dependent variable).

To create a digital recording of the sound wave, we convert the analog sound signal into a digital form by performing sampling. Sampling converts the analog audio signal into a digital signal by taking measurements of the dependent variable...