Book Image

Hands-On Neural Network Programming with C#

By : Matt Cole
Book Image

Hands-On Neural Network Programming with C#

By: Matt Cole

Overview of this book

Neural networks have made a surprise comeback in the last few years and have brought tremendous innovation in the world of artificial intelligence. The goal of this book is to provide C# programmers with practical guidance in solving complex computational challenges using neural networks and C# libraries such as CNTK, and TensorFlowSharp. This book will take you on a step-by-step practical journey, covering everything from the mathematical and theoretical aspects of neural networks, to building your own deep neural networks into your applications with the C# and .NET frameworks. This book begins by giving you a quick refresher of neural networks. You will learn how to build a neural network from scratch using packages such as Encog, Aforge, and Accord. You will learn about various concepts and techniques, such as deep networks, perceptrons, optimization algorithms, convolutional networks, and autoencoders. You will learn ways to add intelligent features to your .NET apps, such as facial and motion detection, object detection and labeling, language understanding, knowledge, and intelligent search. Throughout this book, you will be working on interesting demonstrations that will make it easier to implement complex neural networks in your enterprise applications.
Table of Contents (16 chapters)
13
Activation Function Timings

Replacing back propagation with Particle Swarm Optimization

And now we come to the moment of truth. How does any of this apply to my code? In order to answer this question, we are going to use the open source Encog machine learning framework for our next demonstration. You can download our sample project following the instructions for the web location of the files for the book. Please make sure you have it loaded and open in Visual Studio before proceeding:

We are going to create a sample application that will demonstrate replacing back propagation with Particle Swarm Optimization. If all goes well, from the outside looking in you will not notice a difference.

You will be able to run this sample out of the box and follow along. We will be using the XOR problem solver, but instead of using back propagation it will be using the Particle Swarm Optimization we've been discussing...