In a quick Google for her name I stumbled across this item which
may be worth a look:
<http://web.informatik.uni-bonn.de/II/ag-klein/people/zach/
benchmarks/NeuralNetTest.java>
<snip>
This reminds me of this other nifty demo:
http://psych.rice.edu/mmtbn/
(Use the top of the page menus to go to Chapter: 1. Language,
Section, 7. Word Production II, bottom of the page)
DemoGNG, a Java applet, implements several methods related to
competitive learning. It is possible to experiment with the methods
using various data distributions and observe the learning process.
http://www.neuroinformatik.ruhr-uni-bochum.de/ini/VDM/research/gsn/
DemoGNG/GNG.html
http://www.hav.com/ (click on web demos and neural demos --
javascript NN!)
Package net.openai.ai.nn.architecture
http://openai.sourceforge.net/javadocs/ai/nn/net/openai/ai/nn/
architecture/package-summary.html
More Input! More Input!
An introduction to Neural Networks (1996) Ben Kröse, Patrick van der
Smagt
http://citeseer.csail.mit.edu/ose96introduction.html
A Neural Network Primer (1994) by Hervé Abdi
http://citeseer.ist.psu.edu/190642.html
You have plenty of related papers linked to on that page.
Biological metaphors and the design of artificial neural networks
http://www.liacs.nl/MScThesis/boers-kuiper.html
Neural Networks & Connectionist Systems (page of links)
http://www.aaai.org/AITopics/html/neural.html
A Brief History of Connectionism
http://neuron-ai.tuke.sk/NCS/VOL1/P3_html/vol1_3.html
Connectionism, Confusion, and Cognitive Science
http://www.bcp.psych.ualberta.ca/~mike/Pearl_Street/Papers/Confuse/
confuse.html
Neural Nets, Connectionism, Perceptrons, etc.
http://cscs.umich.edu/~crshalizi/notebooks/neural-nets.html
Introduction to Connectionism
http://www.neuromod.org/courses/connectionism/introduction-to-
connectionism/
... and a lot lot more
the prof said the computer code was too "dense" and referred me to
verbal passages that made no
sense at all
BTW, even better than stepping through the code is taking each
formula, and transforming it into code. You can even do this using
excel, the computations are not very complex to implement as functions.
Honestly, you only make sense of ANN if you get some praticals. (1)
You understand what computation is carried on (2) You play around
with various material, trying to make guesses before you actually run
the simulation. I had been very successful teaching Connectionism
with this model. I had some very unsuccessful (from the point of view
of student understanding) with the "verbal model". On my post at
Edinburgh, I was taking over somebody else course on connectionism.
Six hours of theory and no practicals were adjoined to the course.
Complete aberration and I was not allowed to do anything about it.
Put very simply, what a neural network (standard feedforward one)
does is nothing else than extract the emergent regularities in your
material. What is a lot more interesting is attractors and self-
organisation. But that's a bit more complex to understand and develop
intuitions about. Best is to start with feedforward ones (delta-rule,
backpropagation).
Marielle
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