This is the code repository for Neural Networks with R, published by Packt. It contains all the supporting project files necessary to work through the book from start to finish.
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.
All of the code is organized into folders. Each folder starts with a number followed by the application name. For example, Chapter02.
The code will look like the following:
mydata=read.csv('Squares.csv',sep=",",header=TRUE)
mydata
attach(mydata)
names(mydata)This book is focused on neural networks in an R environment. We have used R version 3.4.1 to build various applications and the open source and enterprise-ready professional software for R, RStudio version 1.0.153. We focus on how to utilize various R libraries in the best possible way to build real-world applications. In that spirit, we have tried to keep all the code as friendly and readable as possible. We feel that this will enable our readers to easily understand the code and readily use it in different scenarios.