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 primary reason for computing the derivative with respect to C before K?

• C is a constant value.

• The derivative with respect to K requires the derivative with respect to C.

• C is easier to compute than K.

• K is not involved in the computation process.

How does Max pooling affect the computation of derivatives?

• It requires additional computational steps.

• It simplifies the computation by focusing only on maximum values.

• It complicates the computation process.

• It makes all derivatives zero.

In a 2x2 Max pooling block, what happens to the gradient of non-maximum entries?

• They are averaged.

• They are ignored.

• They are doubled.

• They are set to zero.

What is the purpose of the function 'Get gradient with respect to C'?

• To compute the forward pass.

• To calculate the gradient with respect to K.

• To initialize the matrix C.

• To determine the gradient with respect to C using Max pooling.

Why is the stride set to two in the Max pooling process?

• To reduce computation time.

• To match the size of the pooling block.

• To ensure non-overlapping pooling regions.

• To cover more area.

What is the significance of unraveling the index in the code?

• To sort the values.

• To initialize the matrix.

• To locate the maximum value's position.

• To find the minimum value.

What is the next step after computing the gradient with respect to C?

• Computing the derivative with respect to K.

• Computing the derivative with respect to B.

• Finalizing the forward pass.

• Ending the computation process.