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

Summary


In this final chapter, we saw some use cases with neural networks and deep learning. This should form the basis of your future work on neural networks. The usage is common in most cases, with changes in the dataset involved for the model during training and testing.

We saw the following examples in this chapter:

  • Integrating TensorFlow and Keras with R, which opens up vast set of use cases to be built using R
  • Building a digit recognizer through classification using H2O
  • Understanding the LSTM function with MxNet
  • PCA using H2O
  • Building an autoencoder using H2O
  • Usage of darch for classification problems

R is a very flexible and a major statistical programming language for data scientists across the world. A grasp of neural networks with R will help the community evolve further and increase the usage of R for deep learning and newer use cases.