Now that we've had a quick refresher on Neural Networks, I thought that perhaps a good starting point, code-wise, would be for us to write a very simple neural network. We're not going to go crazy; we'll just lay the basic framework for a few functions so that you can get a good idea of what is behind the scenes of many of the APIs that you'll use. From start to finish, we'll develop this network application so that you are familiar with all the basic components that are contained in a neural network. This implementation is not perfect or all-encompassing, nor is it meant to be. As I mentioned, this will merely provide a framework for us to use in the rest of the book. This is a very basic neural network with the added functionality of being able to save and load networks and data. In any event, you will have...
Hands-On Neural Network Programming with C#
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Hands-On Neural Network Programming with C#
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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.
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Hands-On Neural Network Programming with C#
Table of Contents (16 chapters)
Preface
Free Chapter
A Quick Refresher
Building Our First Neural Network Together
Decision Trees and Random Forests
Face and Motion Detection
Training CNNs Using ConvNetSharp
Training Autoencoders Using RNNSharp
Replacing Back Propagation with PSO
Function Optimizations: How and Why
Finding Optimal Parameters
Object Detection with TensorFlowSharp
Time Series Prediction and LSTM Using CNTK
GRUs Compared to LSTMs, RNNs, and Feedforward networks
Activation Function Timings
Function Optimization Reference
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