What are some good resources for learning about Artificial Neural Networks? [closed]

Answer 1

Neural Networks are kind of declasse these days. Support vector machines and kernel methods are better for more classes of problems then backpropagation. Neural networks and genetic algorithms capture the imagination of people who don't know much about modern machine learning but they are not state of the art.

If you want to learn more about AI and machine learning, I recommend reading Peter Norvig's Artificial Intelligence: A Modern Approach. It's a broad survey of AI and lots of modern technology. It goes over the history and older techniques too, and will give you a more complete grounding in the basics of AI and machine Learning.

Neural networks are pretty easy, though. Especially if you use a genetic algorithm to determine the weights, rather then proper backpropagation.

Answer 2

If you want to do quickly learn about applications of some neural network concepts on a real simulator, there is a great online book (now wiki) called 'Computational Cognitive Neuroscience' at http://grey.colorado.edu/CompCogNeuro/index.php/CCNBook/Main

The book is used at schools as a textbook, and takes you through lots of different brain areas, from individual neurons all the way to higher-order executive functioning.

In addition, each section is augmented with homework 'projects' that are already down for you. Just download, follow the steps, and simulate everything that the chapter talked about. The software they use, Emergent, is a little finnicky but incredibly robust: its the product of more than 10 years of work I believe.

I went through it in an undergrad class this past semester, and it was great. Walks you through everything step by step

Answer 3

I found Fausett's Fundamentals of Neural Networks a straightforward and easy-to-get-into introductory textbook.

Answer 4

I agree with the other people who said that studying biology is not a good starting point... because theres a lot of irrelevant info in biology. You do not need to understand how a neuron works to recreate its functionality - you only need to simulate its actions. I recomend "How To Create A Mind" by Ray Kurzweil - it goes into the aspect of biology that is relevant for computational models, (creating a simualted neuron by combining several inputs and firing once a threshhold is reached) but ignores the irrelvant stuff like how the neuron actually adds thouse inputs togeather. (You will just use + and an inequality to compare to a threshold, for example)

I should also point out that the book isn't really about 'creating a mind' - it only focuses on heirarchical pattern recognition / the neocortex. The general theme has been talked about since the 1980s I beleive, so there are plenty of older books that probably contain slightly dated forms of the same information. I have read older documents stating that the vision system, for example, is a multi layered pattern recognizer. He contends that this applies to the entire neocortex. Also, take his 'predictions' with a grain of salt - his hardware estimates are probably pretty accurate, but i think he underestimates how complicated simple tasks can be (ex: driving a car). Granted, he has seen a lot of progress (and been part of some of it) but i still think he is over optimistic. There is a big difference between an AI car being able to drive a mile successfully 90% of the time, when compared to the 99.9+% that a human can do. I don't expect any AI to be truly out driving me for atleast 20 years... (I don't count BMWs track cars that need to be 'trained' on the actual course, as they aren't really playing the same game)

If you already have a basic idea of what AI is and how it can be modeled, you may be better off skipping to something more technical.