Anyone know of some good intro to neural networking type books? Something from theory to application, without being too 'dense'. I know they're somewhat dense in general, but nothing with 20 consecutive pages of formulas (which are usually special cases to the special cases that the author just happened to like). I've had some AI and the basics, but a good refresher is always welcome. Please post in the comments section.
KTHNXBYE!
2 comments:
Used this one in a good machine learning survey course last year. Covers theory and practical applications of neural networks, support vector machines, bayesian learning, decision trees, etc. Ranges from a good introduction to in-depth theoretical explanation.
Check it out:
http://www.amazon.com/Machine-Learning-Mcgraw-Hill-International-Edit/dp/0071154671/ref=pd_bbs_sr_1?ie=UTF8&s=books&qid=1197018519&sr=8-1
I actually have a pdf of that book (don't ask how), and I just plowed through the first chapter. I think this is exactly what I was looking for, so thanks! It is a little dated, but it is general enough that the basics haven't gone out of style.
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