Book Image

Neural Networks with R

By : Balaji Venkateswaran, Giuseppe Ciaburro
Book Image

Neural Networks with R

By: Balaji Venkateswaran, Giuseppe Ciaburro

Overview of this book

Neural networks are one of the most fascinating machine learning models for solving complex computational problems efficiently. Neural networks are used to solve wide range of problems in different areas of AI and machine learning. This book explains the niche aspects of neural networking and provides you with foundation to get started with advanced topics. The book begins with neural network design using the neural net package, then you’ll build a solid foundation knowledge of how a neural network learns from data, and the principles behind it. This book covers various types of neural network including recurrent neural networks and convoluted neural networks. You will not only learn how to train neural networks, but will also explore generalization of these networks. Later we will delve into combining different neural network models and work with the real-world use cases. By the end of this book, you will learn to implement neural network models in your applications with the help of practical examples in the book.
Table of Contents (14 chapters)
Title Page
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface

Recurrent Neural Network


Within the set of Artificial Neural Networks (ANN), there are several variants based on the number of hidden layers and data flow. One of the variants is RNN, where the connections between neurons can form a cycle. Unlike feed-forward networks, RNNs can use internal memory for their processing. RNNs are a class of ANNs that feature connections between hidden layers that are propagated through time in order to learn sequences. RNN use cases include the following fields:

  • Stock market predictions
  • Image captioning
  • Weather forecast
  • Time-series-based forecasts
  • Language translation
  • Speech recognition
  • Handwriting recognition
  • Audio or video processing
  • Robotics action sequencing

The networks we have studied so far (feed-forward networks) are based on input data that is powered to the network and converted into output. If it is a supervised learning algorithm, the output is a label that can recognize the input. Basically, these algorithms connect raw data to specific categories by recognizing...