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使用C#:为C#企业应用程序添加强大的神经网络功能-Hands-On Neural Network Programming with C#: Add powerful neural network c文件编号:1045

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标题(title):Hands-On Neural Network Programming with C#: Add powerful neural network capabilities to your C# enterprise applications
使用C#:为C#企业应用程序添加强大的神经网络功能
作者(author):Matt R. Cole
出版社(publisher):Packt Publishing
大小(size):36 MB (37865824 bytes)
格式(extension):epub
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Create and unleash the power of neural networks by implementing C# and .Net code
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.



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