I am writing a neural network class and have come across two concepts I don't understand. Would anyone be able to tell me what the bias and momentum is and does
Bias is a constant input given to neurons. e.g. in a normal feed forward network, you might have 2 input units, 2 hidden units and 1 output unit. a constant bias value (let's say 1) will go into the hidden and output units in addition to the input from the input units.
Momentum is the additional learning rate used at the beginning of learning to make learning faster. e.g. learning error is usually initially very large, so you start with high momentum and adjust weights more aggressively. later on during learning as your error decreases, momentum should also decrease so you learn more slowly but you'll be less likely to overshoot the target.