HOW A NEURAL NET WORKS STEP BY STEP:
There is a matrix of nodes like this:
0 0 0
0 0 0
0 0 0
Inputs are fed in from the left. like this:
(.1) 0 0
(.24) 0 0 (inputs in parenthesis)
(0) 0 0
the next nodes data is calculated by multiplying A weight to each node
in the layer before and then adding bias. And then putting in an
activation function. Like this:
.1 ----(weight: 1)--> .1
.24 ----(weight: .5)--> .12 ----> .1 + .12 + 0 = .22 + bias
0 ----(weight: 2)--> 0
Each node has a bias, for this node lets say its .3
.22 + .3(bias) = .52
After all that you put it in an activation function.
tanh(.52) ---> data for the next node
Everything i showed above was just the caclulation for the
first node in the second layer though so the matrix
would look like this
after all that.
.1 tanh(.52) 0
.24 0 0
0 0 0
Then you keep doing that for each layer until you reach the end.
Hope this helped?
(Q A)
Does each node have a weight?
No. Each connection has a weight. Or i guess you could say each node
has lots of weights each corresponding to a node in the last layer.
Does each node have a bias?
Yes each node has only one bias.