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

Chapter 7. Use Cases of Neural Networks – Advanced Topics

With Artificial Neural Networks (ANN), let's try to simulate typical brain activities such as image perception, pattern recognition, language understanding, sense-motor coordination, and so on. ANN models are composed of a system of nodes, equivalent to neurons of a human brain, which are interconnected by weighted links, equivalent to synapses between neurons. The output of the network is modified iteratively from link weights to convergence.

This final chapter presents ANN applications from different use cases and how neural networks can be used in the AI world. We will see some use cases and their implementation in R. You can adapt the same set of programs for other real work scenarios.

The following topics will be covered:

  • TensorFlow integration with R
  • Keras integration with R
  • Handwritten digit recognition using MNIST dataset with H2O
  • Building LSTM with mxnet
  • Clustering data using auto encoders with H2O
  • Principal Component Analysis (PCA...