Some tutorial Python and Matlab neural network programs
Here are a few extensively commented Python and Matlab programs
that I wrote, which
I hope might be useful for teaching a course
on how to program neural nets.
Please feel free to
mail me with any
questions or comments.

Backpropagation_using_only_NumPy.ipynb. This program shows stepbystep how to write classic Rumelhart & McClelland backprop using just matrices and vectors. This code might be useful for teaching the basic structure of the backprop algorithm, or for showing how to avoid forloops by vectorising. It is a live Python notebook, hosted on Google Colab, so it should run inside any web browser, without needing any additional software (i.e. you don't even need to have Python installed on your computer).

bkprop.m  The original Matlab version.
I posted this code to the newsgroup
comp.ai.neuralnets
and it was subsequently
used for teaching in a course
at the University of Stirling.

sofm.m  This program shows how a SelfOrganising
Feature Map (or SOFM, also often called a Kohonen map) can learn
a particular input distribution, in this case a letter 'A'.

fuzzyartmap_demo.m  This program shows graphically how
Fuzzy ARTMAP learns the circleinthesquare task by growing
category boxes in response to the presented inputs. Detailed comments
explain the algorithm's match, reset and learning stages.