Hi @vishnubhatla , I’m still working with Andrew and Geoff on a version of backprop that will be suitable for our target learner. We had recorded a version of it but decided as a team that it needed more redesign. It will be different from the original in that we’ll avoid matrix multiplication and transpose, and also discuss the computation graph and hopefully the chain rule in order to focus on the visual intuition.
You can take a look at the lectures that Sam referred to as a preview, as we’ll incorporate some of this approach in the backprop lectures when we film it. We’ll work on this after we launch course 3.
Also, you can take a look at these lectures on the derivative of logistic loss. how to get the derivatives of the logistic cost / loss function [TEACHING STAFF].
If you get to the last video, you’ll see how the chain rule lets you reuse a lot of computations of derivatives without needing to re-calculate them, which is really important for efficiency. Backprop is just the application of the chain rule to neural networks (thanks to Geoffrey Hinton’s research), and it’s what makes efficient training of neural networks possible.
Thanks, -Eddy