Simple Neural Network

A fully connected customizable neural network with an example.
Updated 10 Feb 2019

A fully connected neural network with many options for customisation.
Basic training:
modelNN = learnNN(X, y);
p = predictNN(X_valid, modelNN);
One can use an arbitrary number of hidden layers, different activation functions (currently tanh or sigm), custom regularisation parameter, validation sets, etc. The code does not use any matlab toolboxes, therefore, it is perfect if you do not have the statistics and machine learning toolbox, or if you have an older version of matlab. I use the conjugate gradient algorithm for minimisation borrowed from Andrew Ngs machine learning course. See the github and comments in the code for more documentation.

Cite As

Vahe Tshitoyan (2024). Simple Neural Network (, GitHub. Retrieved .

MATLAB Release Compatibility
Created with R2015b
Compatible with any release
Platform Compatibility
Windows macOS Linux
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Version Published Release Notes

Automatically including the "lib" folder.

a fix to confusion matrix

Update the title and the picture
Updated the summary.
basic correction to summary
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Description update

To view or report issues in this GitHub add-on, visit the GitHub Repository.
To view or report issues in this GitHub add-on, visit the GitHub Repository.