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walkingcarcass

neural networks

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i am ignorant. i hate that. can anyone please point out some neural network articles and/or tutorials that will take me up to an intermediate level? ******** A Problem Worthy of Attack Proves It''s Worth by Fighting Back

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Guest Anonymous Poster
You will not get up to an ''intermediate'' level just by reading a few articles/tutorials. Artificial Neural Networks are complicated mathematical modeling devices that few people understand past "I made a 2 layer net that learns XOR." Chances are that if you don''t take graduate school classes in Computational AI and Optimization you will never get to an ''intermediate'' level. But you can still have fun : )

www.generation5.org has some good articles and search the web for the ANN Faq. These are good starting places.

- Good Luck

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Guest Anonymous Poster
quote:
Original post by Raptor85
you can get to an "intermediate" level without any actual classes, it''s just hard as hell to do....


Right. I said "Chances are..." (Though I have only met 2 people who have done so (without classes) - and it is arguable if they are even at an intermediate level)

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Dedication and the willingness to never quit learning are all you need to get to any level with any subject. Only those who put restrictions on learning are the ones that are limited in their potential.

Learn everything you can from every source you can and I guarantee that you can get to the Advanced level without taking one single course. The trick to learning is asking questions; as many questions as you can think of, then when you run out, ask people if they have any questions for you.

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Carc,

I used to feel ignorent about NN as well, visit generation5.org and the AI depot. They are both excellent resources, while you''re there you should read some of their top grade articles/tutorials about Genetic Algors 2.



-Sash
-5

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Most online articles I''ve read tend to dodge certain issues with neural networks. There''s a great article that you can find on google if you search for "Kangaroos and Neural Networks" explaining what they do, although not so much how they do it, definitely not enough to help you implement it. There is a book called Neural Smithing published by MIT Press that is very complete and straightforward. It contains C style pseudocode that has enough to get you started while avoiding details necessary in your final code that distract from the algorithms they are explaining. It starts with perceptrons and explains backprop in full by chapter 5. The rest of the book focuses on ways to speed up training by such things as weight initialization techniques and variations on backprop. Practical Neural Network Recipes in C++ by Tim Masters is also a good book, containing complete source code for a neural network implementation. His explanations through the book are pretty good but the code is messy and is purely procedural. General machine learning books i''ve read usually have a lot of magic math in them and just skirt the implementation of backprop. They''re nice if you want to look into something other than NNs that might be more appropriate for your project.
AI depot and Generation5 are the best AI sites i''ve come across regarding this, and they link to many others, but if you really want to learn this you should invest some money in a book.

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