hmm, i don't really think C# has anything to do with it, other than just providing a vehicle for implementation of fundamental ideas of NN theory (imho ofcourse).
in my opinion, using mathematica (or some such) would have been a far saner choice, to get to the essence of the subject. also, a dosage of classical (pioneering ?) work as described in (parallel-distributed-processing vol-1&2) http://www.amazon.com/Parallel-Distributed-Processing-Vol-Fo..., would be just great.
There's a bunch of free textbooks online, especially for CS and math topics. I'd love to see online communities develop around the textbooks for like-minded people to form study groups.
Look, a neural network is not some magic machine that can solve all your classification problems. A good 90% of applications of ANNs I've seen could (read: should) have been replaced with a support vector machine, or a Bayesian classifier, or some other proper statistically principled model.
I swear, I get the impression that people keep coming back to ANNs just because the goddamn name sounds cool.
Some of us just don't know what they are or how to use them. I guarantee I'll be forming another study group some time in the future on the other topics you mentioned if they are as broad in application as you say. :)
I completely agree with you! The same should be said for genetic algorithms. I don't understand the obsession around here with both ANN and GA. They are interesting tools, but they are not some holy grail with unlimited applications.
Both of them are inefficient and rarely the right tool for the job. GA and ANN are used for situations where you don't care about how you got to the solution and what it took to get there, as long as you get close to it in the end.
[+] [-] almost|17 years ago|reply
Of course C# seems like a horrible choice for learning about this sort of stuff, but maybe that's just me.
[+] [-] signa11|17 years ago|reply
in my opinion, using mathematica (or some such) would have been a far saner choice, to get to the essence of the subject. also, a dosage of classical (pioneering ?) work as described in (parallel-distributed-processing vol-1&2) http://www.amazon.com/Parallel-Distributed-Processing-Vol-Fo..., would be just great.
[+] [-] paulgb|17 years ago|reply
There's a bunch of free textbooks online, especially for CS and math topics. I'd love to see online communities develop around the textbooks for like-minded people to form study groups.
[+] [-] samson|17 years ago|reply
[+] [-] axiom|17 years ago|reply
Look, a neural network is not some magic machine that can solve all your classification problems. A good 90% of applications of ANNs I've seen could (read: should) have been replaced with a support vector machine, or a Bayesian classifier, or some other proper statistically principled model.
I swear, I get the impression that people keep coming back to ANNs just because the goddamn name sounds cool.
[+] [-] jcbozonier|17 years ago|reply
[+] [-] designtofly|17 years ago|reply
Both of them are inefficient and rarely the right tool for the job. GA and ANN are used for situations where you don't care about how you got to the solution and what it took to get there, as long as you get close to it in the end.
[+] [-] psyklic|17 years ago|reply
[+] [-] dustineichler|17 years ago|reply
[+] [-] signa11|17 years ago|reply