Submitted by Matthyou200 t3_zrydfu in deeplearning
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
Nerveregenerator t1_j16yfnw wrote
Write all the equations out one paper, then do one forward and backward pass on paper as well with a simple mlp. I believe bias can be easily incorporated using an extra 1 in the input and using an extra weight as the bias, so it’s updated the same as any other weight. Also learn the basics of matrix multiplication.