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

The video tutorial covers the implementation of the backward pass in a neural network using Numpy. It begins with a brief introduction to the backward pass and the need to differentiate the loss function with respect to parameters. The tutorial then explains how to compute the derivative with respect to 'West' using the chain rule, highlighting the symmetry between derivatives with respect to 'West' and 'F'. The instructor demonstrates coding the derivative function in Python, addressing common errors and debugging tips. The session concludes with a preview of the next steps in the implementation.

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