Data Science and Machine Learning (Theory and Projects) A to Z - Gradient Descent in CNNs: Implementation in NumPy Backw

This video tutorial covers the computation of derivatives using Numpy, focusing on derivatives with respect to BF and South. It includes writing functions to compute these derivatives and reshaping matrices. The tutorial concludes with a preview of the next video, which will address derivatives with respect to C.

What is the main focus of the video tutorial?

• Understanding the concept of Max pooling

• Writing code for derivatives with respect to BF

• Learning about Numpy arrays

• Computing derivatives with respect to West

Which formula is used to compute the derivative with respect to BF?

• Y hat squared minus Y

• Y hat plus Y, divided by Y hat

• Y hat times Y, minus 1

• Y hat minus Y, multiplied by Y hat and 1 - Y hat

What is the purpose of reshaping the derivative with respect to F?

• To compute the derivative with respect to BF

• To simplify the matrix operations

• To compute the derivative with respect to South

• To enhance the computational speed

What is the next topic to be covered in the following video?

• Understanding the concept of Max pooling

• Computing the derivative with respect to C

• Writing code for derivatives with respect to BF

• Learning about Numpy arrays

What is the significance of the square root in reshaping the matrix?

• It determines the number of rows and columns in South

• It simplifies the derivative calculation

• It is used to compute the derivative with respect to BF

• It enhances the computational speed