Deep Learning CNN Convolutional Neural Networks with Python - Implementation in NumPy BackwardPass 2

This video tutorial covers the computation of derivatives using Numpy, focusing on derivatives with respect to various parameters such as BF and S. The instructor demonstrates how to write functions for these computations and implements them in a Jupyter Notebook. The video concludes with a discussion on reshaping matrices for derivatives and previews the next topic on computing derivatives with respect to C.

What is the main focus of the initial part of the video?

• Learning about data structures

• Understanding the basics of calculus

• Introduction to Python programming

• Computing derivatives with respect to West and F

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

• A + B - C

• P / Q * R

• Y hat * Y * (1 - Y hat)

• X * Y * Z

What is the purpose of the function written for BF?

• To sort an array

• To find the maximum value in a list

• To calculate the sum of two numbers

• To compute the derivative with respect to BF

What is the main task in the final section of the video?

• Designing a user interface

• Implementing a sorting algorithm

• Calculating the average of a dataset

• Reshaping the matrix to compute the derivative with respect to the sampling layer

How is the reshaping size determined for the sampling layer derivative?

• By taking the square root of the length of the vector DF

• By adding all elements of the vector

• By multiplying the elements of the vector

• By dividing the vector into equal parts