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

Adding new optimization functions

One of the beautiful things about our visual test workbench is the ease of which we can add new optimization functions for testing.

The purpose of functions

Some problems are evaluated in terms of quality versus correct or incorrect. Such problems are known as optimization problems because the goal is the identification of the optimal value. Functions (sometimes called cost functions, objective functions, error functions, and so on.) achieve that goal by mapping n-dimensional real-valued items into one-dimensional real-valued items (some folks will prefer the termspaces over items' as it more closely aligns with the total search space we have talked about).

There are two types of function...