November 2017
Beginner to intermediate
366 pages
7h 59m
English
The input x1, x2 is multiplied by the weights w1..w4 in each node and the respective bias is added. This is the linear transformation:
An activation function is applied on top to make it a non-linear transformation. A sigmoid transformation looks like this:
Now these activations are multiplied by v1..v4 and the respective bias is added:
Once again, the activation is applied on top of it:
A softmax function is applied, finally, ...
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