Book Description
Uncover the power of artificial neural networks by implementing them through R code.
About This Book
 Develop a strong background in neural networks with R, to implement them in your applications
 Build smart systems using the power of deep learning
 Realworld case studies to illustrate the power of neural network models
Who This Book Is For
This book is intended for anyone who has a statistical background with knowledge in R and wants to work with neural networks to get better results from complex data. If you are interested in artificial intelligence and deep learning and you want to level up, then this book is what you need!
What You Will Learn
 Set up R packages for neural networks and deep learning
 Understand the core concepts of artificial neural networks
 Understand neurons, perceptrons, bias, weights, and activation functions
 Implement supervised and unsupervised machine learning in R for neural networks
 Predict and classify data automatically using neural networks
 Evaluate and finetune the models you build.
In Detail
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 realworld 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.
Style and approach
A stepbystep guide filled with realworld practical examples.
Publisher Resources
Table of Contents
 Preface

Neural Network and Artificial Intelligence Concepts
 Introduction
 Inspiration for neural networks
 How do neural networks work?
 Layered approach
 Weights and biases
 Training neural networks
 Epoch
 Activation functions
 Different activation functions
 Which activation functions to use?
 Perceptron and multilayer architectures
 Forward and backpropagation
 Stepbystep illustration of a neuralnet and an activation function
 Feedforward and feedback networks
 Gradient descent
 Taxonomy of neural networks
 Simple example using R neural net library  neuralnet()
 Implementation using nnet() library
 Deep learning
 Pros and cons of neural networks
 Best practices in neural network implementations
 Quick note on GPU processing
 Summary

Learning Process in Neural Networks
 What is machine learning?
 Supervised learning
 Unsupervised learning
 Reinforcement learning
 Training and testing the model
 The data cycle
 Evaluation metrics
 Learning in neural networks
 Back to backpropagation
 Neural network learning algorithm optimization
 Supervised learning in neural networks
 Unsupervised learning in neural networks
 Summary
 Deep Learning Using Multilayer Neural Networks
 Perceptron Neural Network Modeling – Basic Models
 Training and Visualizing a Neural Network in R
 Recurrent and Convolutional Neural Networks
 Use Cases of Neural Networks – Advanced Topics
Product Information
 Title: Neural Networks with R
 Author(s):
 Release date: September 2017
 Publisher(s): Packt Publishing
 ISBN: 9781788397872