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Full Description
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 DescriptionNeural 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 forThis 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
Contents
Table of Contents
A Quick Refresher
Building our first Neural Network Together
Decision Tress 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
Appendix A- Activation Function Timings
Appendix B- Function Optimization Reference
