Non-mathematical Description of Neural Networks

Question

I am not a mathematician. I enjoy a good math puzzle, but I admit my weaknesses whole heartedly. That said, I've always had an interest in Neural Networks, and while I understand them enough to implement them from scratch, I hit a wall when I need to understand any concept that I can only find mathematic proofs for. Where is the programmer's guide to neural networks, using code instead of formula to explain the practical reasonings?

Answer 1

Another alternative is a non-math, non-programming explanation. The book Blondie24: Playing at the Edge of AI contains a really great explanation of neural networks. It's about a checkers-playing AI developed by the author. It's not completely without programming references, but it does a great job of explaining how the algorithms work without getting into the code of the solution.

Answer 2

Unfortunately, I don't know if there's a good single "programmers source" that will give you all of the concepts. I liked Neural and Adaptive Systems: Fundamentals through Simulations.

The best way to have a "programmer's understanding" of neural networks is not so much by examining the code, but in the problem and the correct results. So, if you don't want to look at math, I recommend you look at a given problem. For example, consider the XOR problem as an example of why you need non-linear activation functions, look at the number of variables and their possible values for understanding why a neural network needs to be of a certain size and toplogy to be effective, and split your data into train/test regimes and do studies to see why overfitting is dangerous. Examine the code with the data.

I also recommend not getting too hung up, but reading further. Certain practices in feed-forward networks become more clear once you see their generalization in recurrent and constructive neural networks. I also recommend going wider: Bayesian networks, fuzzy cognitive maps, SOM, Boltzman machines, simulated annealing, and reinforcement learning all have intuitions.

Does this go towards answering your question?

Answer 3

You need a bit of understanding in either Pascal or Delphi but this overview from ThinkQuest is pretty helpful from a programming stand-point. It also explains some of the difficulties and why the mathematics looks a bit intimidating. (I'm not a mathematician either.)

I was pretty interested in this sort of thing a while back (still am for the most part) and in search of some walk-throughs that I can follow pretty quickly.

Hope that helps a little at least.

Answer 4

I have personally used:

Practical Neural Network Recipes in C++

http://www.amazon.com/Practical-Neural-Network-Recipes-C/dp/0124790402/ref=pd_bxgy_b_img_b/179-4083507-8029219

The author in my opinion does not fully utilize the more powerful functionality of C++, in many cases it reads more like traditional C with classes. The book is also a little dated by now.

HOWEVER - if you need explanations of the algorithms and techniques used in neural networks, explained in a way that an intelligent layperson could understand, so that you can go away and try these things for yourself, then I would certainly give this book a try. Not much navel-gazing goes on here, which is what I liked.

It takes you through all the main things needed to program a neural network - how to compare the actual output with the desired in order to obtain an error signal, and then use this error signal in conjunction with back propagation algorithms to modify the network link connection strengths, doing this iteratively so that gradually the neural network 'learns' the task.

Answer 5

I did an article with almost exactly this title. The article describes how data is represented to a neural network, as well as other machine learning methods, such as support vector machines.

http://www.heatonresearch.com/content/non-mathematical-introduction-using-neural-networks