This is one thing in my beginning of understanding neural networks is I don't quite understand what to initially set a "bias" at? I understand the Perceptron calculates it's output based on:
P * W + b > 0
and then you could calculate a learning pattern based on b = b + [ G - O ] where G is the Correct Output, and O is the actual Output (1 or 0) to calculate a new bias...but what about an initial bias.....I don't understand how this is calculated, or what initial value should be used besides just "guessing", is there any type of formula for this?
Pardon if I'm mistaken on anything, I'm still learning the whole Neural network idea before I implement my own (crappy) one.
The same goes for learning rate.....I mean most books and such just kinda "pick one" for μ.