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Hands-On Neural Network Programming with C#

You're reading from Hands-On Neural Network Programming with C#

Product type Book

Published in Sep 2018

Publisher Packt

ISBN-13 9781789612011

Pages 328 pages

Edition 1st Edition

Languages

Concepts

Neural Networks

Author (1):

Matt Cole

Table of Contents (16) Chapters

Preface

1. A Quick Refresher

2. Building Our First Neural Network Together

3. Decision Trees 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. Activation Function Timings

14. Function Optimization Reference

15. Other Books You May Enjoy

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Summary

In this chapter, we used the open source package ConvNetSharp to explain CNNs. We looked at how to test and train these networks and also learned why they are convolutional. We worked with several example applications to explain how ConvNetSharp functions and operates. In the next chapter, we will look at autoencoders and RNNSharp, exposing you further to recurrent neural networks.

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Authors (1)

Matt Cole

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.

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