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

How do neural networks work?


Similar to the biological neuron structure, ANNs define the neuron as a central processing unit, which performs a mathematical operation to generate one output from a set of inputs. The output of a neuron is a function of the weighted sum of the inputs plus the bias. Each neuron performs a very simple operation that involves activating if the total amount of signal received exceeds an activation threshold, as shown in the following figure:

The function of the entire neural network is simply the computation of the outputs of all the neurons, which is an entirely deterministic calculation. Essentially, ANN is a set of mathematical function approximations. We would now be introducing new terminology associated with ANNs:

  • Input layer
  • Hidden layer
  • Output layer
  • Weights
  • Bias
  • Activation functions