Unlike LSTMs, GRUs do not use a memory unit to control the flow of information and can directly make use of all the hidden states. Instead of using a cell state, GRUs use the hidden state to transfer information. GRUs usually train faster than other memory-based neural networks because of the fact that they have fewer parameters to train, fewer tensor operations, and can work well with fewer data. GRUs have two gates. These are known as the *reset gate* and the *update gate*. The reset gate is used to determine how to combine new inputs with the previous memory, while the update gate determines how much information to keep from the previous state. If you compare this with LSTMs, the update gate in a GRU is comparable to what the input and forget gates do in an LSTM. It decides what information to add or remove. GRUs also merge the...

#### 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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