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)

Denoising autoencoders

Autoencoders are widely used for feature selection and extraction. They try to apply transformations on the input data to reconstruct the input accurately. When the nodes of the hidden layers are equal to or more than the nodes in the input layer, autoencoders carry the risk of learning the identity function where the output simply equals the input, hence making the autoencoder of no use. Denoising refers to adding random noise to the raw input intentionally before feeding it to the network. By doing this, the identity-function risk is addressed, and the encoder learns significant features from the data and learns a robust representation of the input data. While working with denoising autoencoders, it is essential to note that the loss function is calculated by comparing the output values with the original input and not with the corrupted input.

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