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Hands-On Neural Network Programming with C#

By : Matt Cole

2 (1)

Hands-On Neural Network Programming with C#

2 (1)

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.

Preface

Preface

Who this book is for

What this book covers

To get the most out of this book

Code in Action

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Free Chapter

A Quick Refresher

A Quick Refresher

Technical requirements

Neural network overview

The role of neural networks in today's enterprises

Types of learning

Understanding perceptrons

Understanding activation functions

Understanding back propagation

Summary

References

Building Our First Neural Network Together

Building Our First Neural Network Together

Technical requirements

Our neural network

Neural network training

Neural network functions

The neural network

Examples

Summary

Decision Trees and Random Forests

Decision Trees and Random Forests

Technical requirements

Decision trees

Random forests

SharpLearning

Example code and applications

Summary

References

Face and Motion Detection

Face and Motion Detection

Technical requirements

Facial detection

Motion detection

Summary

Training CNNs Using ConvNetSharp

Training CNNs Using ConvNetSharp

Technical requirements

Getting acquainted

Filters

Creating a network

GPU

Fluent training with the MNIST database

Training the network

Summary

References

Training Autoencoders Using RNNSharp

Training Autoencoders Using RNNSharp

Technical requirements

What is an autoencoder?

Different types of autoencoder

Creating your own autoencoder

Summary

References

Replacing Back Propagation with PSO

Replacing Back Propagation with PSO

Technical requirements

Basic theory

Replacing back propagation with Particle Swarm Optimization

Summary

Function Optimizations: How and Why

Function Optimizations: How and Why

Technical requirements

Getting started

Function minimization and maximization

Hyperparameters and tuning

Understanding visualizations

Plotting results

Adding new optimization functions

Summary

Finding Optimal Parameters

Finding Optimal Parameters

Technical requirements

Optimization

Optimization methods

Parallelism

Summary

References

Object Detection with TensorFlowSharp

Object Detection with TensorFlowSharp

Technical requirements

Working with Tensors

Developing your own TensorFlow application

Detecting images

Summary

References

Time Series Prediction and LSTM Using CNTK

Time Series Prediction and LSTM Using CNTK

Technical requirements

Long short-term memory

CNTK terminology

Our example

How well do LSTMs perform?

Summary

References

GRUs Compared to LSTMs, RNNs, and Feedforward networks

GRUs Compared to LSTMs, RNNs, and Feedforward networks

Technical requirements

QuickNN

Understanding GRUs

Differences between LSTM and GRU

Coding different networks

Comparing LSTM, GRU, Feedforward, and RNN operations

Network differences

Summary

Activation Function Timings

Activation Function Timings

Function Optimization Reference

Function Optimization Reference

The Currin Exponential function

The Webster function

The Oakley & O'Hagan function

The Grammacy function

Franke's function

The Lim function

The Ackley function

The Bukin function N6

The Cross-In-Tray function

The Drop-Wave function

The Eggholder function

The Holder Table function

The Levy function

The Levy function N13

The Rastrigin function

The Schaffer function N.2

The Schaffer function N.4

The Shubert function

The Rotated Hyper-Ellipsoid function

The Sum Squares function

The Booth function

The Mccormick function

The Power Sum function

The Three-Hump Camel function

The Easom function

The Michalewicz function

The Beale function

The Goldstein-Price function

The Perm function

The Griewank function

The Bohachevsky function

The Sphere function

The Rosenbrock function

The Styblinski-Tang function

Summary

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Different types of autoencoder

The following are different types of autoencoder:

Let's briefly discuss autoencoders and the variants we have just seen. Please note that there are other variants out there; these are just probably the most common that I thought you should at least be familiar with.

Standard autoencoder

An autoencoder learns to compress data from the input layer into smaller code, and then uncompress that code into something that (hopefully) matches the original data. The basic idea behind a standard autoencoder is to encode information automatically, hence the name. The entire network always resembles an hourglass, in terms of its shape, with fewer hidden layers than input and output layers. Everything...

Visually different images

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