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.

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Table of Contents (11 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Sections
Get in touch
1
Understanding Neural Networks and Deep Neural Networks
Understanding Neural Networks and Deep Neural Networks
Setting up the environment
Implementing neural networks with Keras
Sequential API
Functional API
TensorFlow Estimator API
TensorFlow Core API
Implementing a single-layer neural network
Training your first deep neural network
Free Chapter
2
Working with Convolutional Neural Networks
Working with Convolutional Neural Networks
Introduction to convolutional operations
Understanding strides and padding
Getting familiar with pooling layers
Implementing transfer learning
3
Recurrent Neural Networks in Action
Recurrent Neural Networks in Action
Sentiment classification using RNNs
Text generation using LSTMs
Time series forecasting using GRUs
Implementing bidirectional recurrent neural networks
4
Implementing Autoencoders with Keras
Implementing Autoencoders with Keras
Implementing vanilla autoencoders
Dimensionality reduction using autoencoders
Denoising autoencoders
Changing black and white into color
5
Deep Generative Models
Deep Generative Models
Generating images with GANs
Implementing DCGANs
Implementing variational autoencoders
6
Handling Big Data Using Large-Scale Deep Learning
Handling Big Data Using Large-Scale Deep Learning
Deep learning on Amazon Web Services
Deep learning on Microsoft Azure
Deep learning on Google Cloud Platform
Accelerating with MXNet
Implementing a deep neural network using MXNet 
Forecasting with MXNet
7
Working with Text and Audio for NLP
Working with Text and Audio for NLP
Neural machine translation
Summarizing text using deep learning
Speech recognition
8
Deep Learning for Computer Vision
Deep Learning for Computer Vision
Object localization
Face recognition
9
Implementing Reinforcement Learning
Implementing Reinforcement Learning
Model-based RL using MDPtoolbox
Model-free RL
Cliff walking using RL
10
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Implementing variational autoencoders

In Chapter 4, Implementing Autoencoders with Keras, we learned about autoencoders. We know that an autoencoder learns to represent input data in a latent feature space of reduced dimensions. It learns an arbitrary function to express input data in a compressed latent representation. A variational autoencoder (VAE), instead of learning an arbitrary function, learns the parameters of the probability distribution of the compressed representation. If we can sample points from this distribution, we can generate new data. A VAE consists of an encoder network and a decoder network.

The structure of a VAE is illustrated in the following diagram:

Let's understand the roles of encoder and decoder networks in a VAE:

  • Encoder: This is a neural network that takes an input, , and outputs a latent representation, . The goal of an encoder...
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