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

Taxonomy of neural networks


The basic foundation for ANNs is the same, but various neural network models have been designed during its evolution. The following are a few of the ANN models:

  • Adaptive Linear Element (ADALINE), is a simple perceptron which can solve only linear problems. Each neuron takes the weighted linear sum of the inputs and passes it to a bi-polar function, which either produces a +1 or -1 depending on the sum. The function checks the sum of the inputs passed and if the net is >= 0, it is +1, else it is -1.
  • Multiple ADALINEs (MADALINE), is a multilayer network of ADALINE units.
  • Perceptrons are single layer neural networks (single neuron or unit), where the input is multidimensional (vector) and the output is a function on the weight sum of the inputs.
  • Radial basis function network is an ANN where a radial basis function is used as an activation function. The network output is a linear combination of radial basis functions of the inputs and some neuron parameters.
  • Feed-forward is the simplest form of neural networks. The data is processed across layers without any loops are cycles. We will study the following feed- forward networks in this book:
    • Autoencoder
    • Probabilistic
    • Time delay
    • Covolutional
  • Recurrent Neural Networks (RNNs), unlike feed-forward networks, propagate data forward and also backwards from later processing stages to earlier stages. The following are the types of RNNs; we shall study them in our later chapters:
    • Hopfield networks
    • Boltzmann machine
    • Self Organizing Maps (SOMs)
    • Bidirectional Associative Memory (BAM)
    • Long Short Term Memory (LSTM)

The following images depict (a) Recurrent neural network and (b) Forward neural network: