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

Getting acquainted

Before we begin diving into code, let's cover some basic terminology so that we are all on the same page when referring to things. This terminology applies to CNNs as well as the ConvNetSharp framework.

Convolution: In mathematics, a convolution is an operation performed on two functions. This operation produces a third function, which is an expression of how the shape of one is modified by the other. This is represented visually in the following diagram:

It is important to note that the convolutional layer itself is the building block of a CNN. This layer's parameters consist of a set of learnable filters (sometimes called kernels). These kernels have a small receptive field, which is a smaller view into the total image, and this view extends through the full depth of the input volume. During the forward propagation phase, each filter is convolved...