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

Coding different networks

In this section, we are going to look at the sample code we described earlier in this chapter. We specifically are going to look at how we build different networks. The NetworkBuilder is our main object for building the four different types of networks we need for this exercise. You can feel free to modify it and add additional networks if you so desire. Currently, it supports the following networks:

  • LSTM
  • RNN
  • GRU
  • Feedforward

The one thing that you will notice in our sample network is that the only difference between networks is how the network itself is created via the NetworkBuilder. All the remaining code stays the same. You will also note if you look through the example source code that the number of iterations or epochs is much lower in the GRU sample. This is because GRUs are typically easier to train and therefore require fewer iterations. While...