Week 2 - Part 2 #35
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Week 2 - Part 2 #35
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Computing gradients for NN modules and Practical tricks for Back Propagation
We begin with a concrete example of backpropagation and discuss the dimensions of Jacobian matrices. We then look at various basic neural net modules and compute their gradients, followed by a brief discussion on softmax and logsoftmax. The other topic of discussion in this part is Practical Tricks for Backpropagation.