This tutorial is particularly troubling, as the two examples are poorly chosen at best and dangerous at worst. The very basics of neural networks are not very t

This tutorial is particularly troubling, as the two examples are poorly chosen at best and dangerous at worst. The very basics of neural networks are not very tough, as it's just a black box where you give it some set of real-valued inputs and it learns to map to a set of real-valued outputs. Most people will never get a neural network to function well in a data mining context due to over-fitting. My issue with the two specific examples are: 1) Image recognition is done in so many different ways, but if you're using neural networks, why not take advantage of the geometry of the layout while simultaneously removing the need for learning about things like hidden node layers. Use HyperNEAT: http://eplex.cs.ucf.edu/hyperNEATpage/HyperNEAT.html http://eplex.cs.ucf.edu/hyperNEATpage/HyperNEAT.html 2) Financial data mining is potentially very dangerous. It's easy for people to create models that look very strong in back-testing but are actually just fitting to the relatively limited history of samples. Some people may be foolish enough to think they should take that signal and trade it with their own money-- yikes! A much better introduction to ANNs would be in a reinforcement learning context, where you aren't worried about over-fitting as much. Mat Buckland's book does a pretty good job of covering these topics: http://www.amazon.com/Techniques-Programming-Premier-Press-Development/dp/193184108X http://www.amazon.com/Techniques-Programming-Premier-Press-D...