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Deep Learning with R Cookbook

By : Gupta, Ansari, Sarkar

5 (3)

Buy this Book

Deep Learning with R Cookbook

5 (3)

By: Gupta, Ansari, Sarkar

Buy this Book

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.

Preface

Who this book is for

What this book covers

To get the most out of this book

Sections

Get in touch

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

Working with Convolutional Neural Networks

Introduction to convolutional operations

Understanding strides and padding

Getting familiar with pooling layers

Implementing transfer learning

Recurrent Neural Networks in Action

Sentiment classification using RNNs

Text generation using LSTMs

Time series forecasting using GRUs

Implementing bidirectional recurrent neural networks

Implementing Autoencoders with Keras

Implementing vanilla autoencoders

Dimensionality reduction using autoencoders

Denoising autoencoders

Changing black and white into color

Deep Generative Models

Generating images with GANs

Implementing DCGANs

Implementing variational autoencoders

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

Working with Text and Audio for NLP

Neural machine translation

Summarizing text using deep learning

Speech recognition

Deep Learning for Computer Vision

Object localization

Face recognition

Implementing Reinforcement Learning

Model-based RL using MDPtoolbox

Model-free RL

Cliff walking using RL

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Sentiment classification using RNNs

RNN is a unique network because of its ability to remember inputs. This ability makes it perfectly suited for problems that deal with sequential data, such as time series forecasting, speech recognition, machine translation, and audio and video sequence prediction. In RNNs, data traverses in such a way that, at each node, the network learns from both the current and previous inputs, sharing the weights over time. It's like performing the same task at each step, just with different inputs that reduce the total number of parameters we need to learn.

For example, if the activation function is tanh, then the weight at the recurrent neuron is and the weight at the input neuron is . Here, we can write the equation for the state, , at time t as follows: