Hello all,

I am a Master's degree student and dont understand backpropagation which is IMO the only hard part in Introduction to neural network learning. When I thought that I understand it, the practical python part proved me wrong: the details with the dimensionalities of the vectors, how to exactly update the bias etc. did not work right.

So how do you guys really internalized it? I somehow know that I don't get it, like on the level to answer twisted exam questions. Some tips how to understand it better (my University does the course very similar to Stanford)

Some help would be very much appreciated :D