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.

Preface

Who this book is for

What this book covers

To get the most out of this book

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

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

Decision Trees and Random Forests

Decision Trees and Random Forests

Technical requirements

Example code and applications

Face and Motion Detection

Face and Motion Detection

Technical requirements

Facial detection

Motion detection

Training CNNs Using ConvNetSharp

Training CNNs Using ConvNetSharp

Technical requirements

Getting acquainted

Creating a network

Fluent training with the MNIST database

Training the network

Training Autoencoders Using RNNSharp

Training Autoencoders Using RNNSharp

Technical requirements

What is an autoencoder?

Different types of autoencoder

Creating your own autoencoder

Replacing Back Propagation with PSO

Replacing Back Propagation with PSO

Technical requirements

Replacing back propagation with Particle Swarm Optimization

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

Finding Optimal Parameters

Finding Optimal Parameters

Technical requirements

Optimization methods

Object Detection with TensorFlowSharp

Object Detection with TensorFlowSharp

Technical requirements

Working with Tensors

Developing your own TensorFlow application

Detecting images

Time Series Prediction and LSTM Using CNTK

Time Series Prediction and LSTM Using CNTK

Technical requirements

Long short-term memory

CNTK terminology

How well do LSTMs perform?

GRUs Compared to LSTMs, RNNs, and Feedforward networks

GRUs Compared to LSTMs, RNNs, and Feedforward networks

Technical requirements

Understanding GRUs

Differences between LSTM and GRU

Coding different networks

Comparing LSTM, GRU, Feedforward, and RNN operations

Network differences

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

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CNTK terminology

It is important that we understand some of the terminology used within the Microsoft CNTK toolkit. Let’s look at some of that terminology now:

Tensors: All CNTK inputs, outputs and parameters are organized as tensors. It should also be noted that minibatches are tensors as well.
Rank: Each tensor has a rank. Scalars are tensors with a rank of 0, vectors are tensors and have a rank of 1, and matrices are tensors with a rank of 2.
Static axis: The dimensions listed in 2 are referred to as axes. Every tensor has static and dynamic axes. A Static axis has the same length throughout its entire life.
Dynamic axis: Dynamic axes, however, can vary their length from instance to instance. Their length is typically not known before each minibatch is presented. Additionally, they may be ordered.
Minibatch: A minibatch is also a tensor. It has a dynamic axis, which...