Recurrent neural networks face difficulties in carrying information properly, especially when there are long order dependencies between layers in large networks. **Long-short term memory** networks, generally referred to as **LSTM** networks, are an extension of RNNs that are capable of learning long-term dependencies and are widely used in deep learning to avoid the vanishing gradient problem that's faced by RNNs. LSTMs combat vanishing gradients through a gating mechanism and are able to remove or add information to the cell state. This cell state is carefully regulated by the gates, which control the information that's passed through them. LSTMs have three kinds of gates: input, output, and forget. The forget gate controls how much information from the previous state we want to pass to the next cell. The input state defines how much information...

#### Deep Learning with R Cookbook

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#### Deep Learning with R Cookbook

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#### 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)

Preface

Understanding Neural Networks and Deep Neural Networks

Free Chapter

Working with Convolutional Neural Networks

Recurrent Neural Networks in Action

Implementing Autoencoders with Keras

Deep Generative Models

Handling Big Data Using Large-Scale Deep Learning

Working with Text and Audio for NLP

Deep Learning for Computer Vision

Implementing Reinforcement Learning

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