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Hands-On Neural Network Programming with C#
Hands-On Neural Network Programming with C#

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

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Profile Icon Matt Cole
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Full star icon Full star icon Empty star icon Empty star icon Empty star icon 2 (1 Ratings)
Paperback Sep 2018 328 pages 1st Edition
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Can$30.99 Can$44.99
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Arrow left icon
Profile Icon Matt Cole
Arrow right icon
Free Trial
Full star icon Full star icon Empty star icon Empty star icon Empty star icon 2 (1 Ratings)
Paperback Sep 2018 328 pages 1st Edition
eBook
Can$30.99 Can$44.99
Paperback
Can$55.99
Subscription
Free Trial
eBook
Can$30.99 Can$44.99
Paperback
Can$55.99
Subscription
Free Trial

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Hands-On Neural Network Programming with C#

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Key benefits

  • 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

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.

Who is this book 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

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.

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Sep 29, 2018
Length: 328 pages
Edition : 1st
Language : English
ISBN-13 : 9781789612011
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Product Details

Publication date : Sep 29, 2018
Length: 328 pages
Edition : 1st
Language : English
ISBN-13 : 9781789612011
Category :
Languages :
Tools :

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Frequently bought together


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Hands-On Neural Network Programming with C#
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Table of Contents

15 Chapters
A Quick Refresher Chevron down icon Chevron up icon
Building Our First Neural Network Together Chevron down icon Chevron up icon
Decision Trees and Random Forests Chevron down icon Chevron up icon
Face and Motion Detection Chevron down icon Chevron up icon
Training CNNs Using ConvNetSharp Chevron down icon Chevron up icon
Training Autoencoders Using RNNSharp Chevron down icon Chevron up icon
Replacing Back Propagation with PSO Chevron down icon Chevron up icon
Function Optimizations: How and Why Chevron down icon Chevron up icon
Finding Optimal Parameters Chevron down icon Chevron up icon
Object Detection with TensorFlowSharp Chevron down icon Chevron up icon
Time Series Prediction and LSTM Using CNTK Chevron down icon Chevron up icon
GRUs Compared to LSTMs, RNNs, and Feedforward networks Chevron down icon Chevron up icon
Activation Function Timings Chevron down icon Chevron up icon
Function Optimization Reference Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

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MrBigBeast Feb 10, 2020
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The book presents (sort of) code for a neural network.The good: it's all in C# without requiring outside packages. Interesting reference stuff towards the end.the bad: the code is cute, using lots of little C# specific tricks. Good luck porting this Java. One class appears on page 35, then its constructor is introduced a dozen or so pages later. Why? Code notes aren't commented.Why couldn't the author just cut and paste his actual code?The Packt Publishing outfit claims you can download the code from their web site. The site is dead. Once you register and agree to allow cookies, nothing works. The links that do respond take forever. Not a good sign for a publisher of programming books...
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