OK, pop quiz time. What do a flock of birds, a school of fish, and a swarm of bees all have in common? Swarm intelligence—knowing how to cooperatively live and work near each other while optimally achieving the same objective. It's not about the intelligence of the individual, but rather the achievements of the group. No one individual has a clear path or directive, no one is at the top shouting orders, yet the optimal goal is always accomplished. Swarms of bees find new nests by doing waggle dances. Birds fly in great harmony, each taking turns being the leader. Fish swim collectively in beautiful architectures we call schools. But if we as humans always need someone at the top giving orders, and we still collectively don't always agree, how is it that millions of these little creatures have been doing it for years and we can't? Oops, going...
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Hands-On Neural Network Programming with C#
By :
Hands-On Neural Network Programming with C#
By:
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)
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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