Pattern Recognition using Artificial Neural Network

A pattern recognition software I wrote in C# using a three-layers neural network with backpropagation.

It was generally supposed to be an Optical Character Recognition software, but it works for other simple patterns as well, such as, happy smiley or sad smiley.
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Lastest Time Series Prediction Neural Network auctions

Most popular time series prediction neural network eBay auctions:

[wprebay kw=“time+series+prediction+neural+network“ num=“0″ ebcat=“267″]
[wprebay kw=“time+series+prediction+neural+network“ num=“1″ ebcat=“267″]

Stock Price Speculation choosing a Continual Neural Network

This video is about how to predict the stock price of a company using a recurrent neural network. We will learn how to create our features and label and how to create a recurrent neural network using Keras.

Please subscribe. That would make me happy and encourage me to keep making my content better and better.

The code for this video:
https://github.com/TannerGilbert/Tutorials/blob/master/Keras-Tutorials/5.%20Stock%20Price%20Prediction%20using%20a%20Recurrent%20Neural%20Network/Stock%20Price%20Prediction.ipynb

Twitter: https://twitter.com/Tanner__Gilbert
Github: https://github.com/TannerGilbert
Website: https://gilberttanner.com/
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Compelling Neural Network Framework By using Stochastic Rewiring

Dynamic Neural Network Structures Through Stochastic Rewiring

Robert Legenstein, Graz University of Technology
https://simons.berkeley.edu/talks/robert-legenstein-4-17-18
Computational Theories of the Brain

Soledad Villar: "Graph neural networks for combinatorial optimization problems"

Machine Learning for Physics and the Physics of Learning 2019
Workshop IV: Using Physical Insights for Machine Learning

„Graph neural networks for combinatorial optimization problems“
Soledad Villar – New York University

Abstract: Graph neural networks are natural objects to express functions on graphs with relevant symmetries. In this talk we introduce graph neural networks, motivated by techniques in statistical physics. We explain how they are being used to learn algorithms for combinatorial optimization problems on graphs from data (like clustering, max-cut and quadratic assignment), in supervised and unsupervised manners. We also show a connection between universal approximation of invariant functions and the graph isomorphism problem.

Institute for Pure and Applied Mathematics, UCLA
November 21, 2019

For more information: http://www.ipam.ucla.edu/mlpws4
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