2018 PB Hands-On Neural Network Programming with C#: Add powerful neural network capabilities to your C# enterprise applications

Create and unleash the power of neural networks by implementing C# and .Net code

Key Features

• Get a strong foundation of neural networks with access to various machine learning and deep learning libraries
• Real-world case studies illustrating various neural network techniques and architectures used by practitioners
• Cutting-edge coverage of Deep Networks, optimization algorithms, convolutional networks, autoencoders and many more

Book Description

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.

What you will learn

• Understand perceptrons and how to implement them in C#
• Learn how to train and visualize a neural network using cognitive services
• Perform image recognition for detecting and labeling objects using C# and TensorFlowSharp
• Detect specific image characteristics such as a face using Accord.Net
• Demonstrate particle swarm optimization using a simple XOR problem and Encog
• Train convolutional neural networks using ConvNetSharp
• Find optimal parameters for your neural network functions using numeric and heuristic optimization techniques.

Who this book is for

This book is for Machine Learning Engineers, Data Scientists, Deep Learning Aspirants and Data Analysts who are now looking to move into advanced machine learning and deep learning with C#. Prior knowledge of machine learning and working experience with C# programming is required to take most out of this book

Table of Contents

1. A Quick Refresher
2. Building our first Neural Network Together
3. Decision Tress and Random Forests
4. Face and Motion Detection
5. Training CNNs using ConvNetSharp
6. Training Autoencoders Using RNNSharp
7. Replacing Back Propagation with PSO
8. Function Optimizations; How and Why
9. Finding Optimal Parameters
10. Object Detection with TensorFlowSharp
11. Time Series Prediction and LSTM Using CNTK
12. GRUs Compared to LSTMs, RNNs, and Feedforward Networks
13. Appendix A- Activation Function Timings
14. Appendix B- Function Optimization Reference

Matt R. Cole is a developer and author with 30 years' experience. Matt is the owner of Evolved AI Solutions, a provider of advanced Machine Learning/Bio-AI, Microservice and Swarm technologies. Matt is recognized as a leader in Microservice and Artificial Intelligence development and design. As an early pioneer of VOIP, Matt developed the VOIP system for NASA for the International Space Station and Space Shuttle. Matt also developed the first Bio Artificial Intelligence framework which completely integrates mirror and canonical neurons. In his spare time Matt authors books, and continues his education taking every available course in advanced mathematics, AI/ML/DL, Quantum Mechanics/Physics, String Theory and Computational Neuroscience.