Deep Learning CNN Convolutional Neural Networks with Python - Implementation in NumPy BackwardPass 3

The video tutorial covers the computation of derivatives with respect to various variables, focusing on the use of max pooling in simplifying the process. It explains the concept of max pooling, its impact on gradients, and how non-maximum entries do not affect the loss function. The tutorial then transitions into implementing a function to compute derivatives with respect to C, using a step-by-step coding approach in Jupyter. The video concludes with a setup for computing derivatives with respect to K, to be continued in the next session.

What is the relationship between the maximum entry and the loss function during backpropagation?

In the implementation of the derivative computation, what role does the shape of the matrix C play?

What steps are involved in determining the index of the maximum value in a pooling block?