Data Science and Machine Learning (Theory and Projects) A to Z - Gradient Descent in CNNs: What is Chain Rule

The video tutorial continues from the previous lesson, focusing on derivatives and their significance in gradient descent algorithms. It explains the initialization of parameters and the iterative process of minimizing the loss function using derivatives. The tutorial also covers the chain rule, emphasizing its importance in computing derivatives for complex neural networks. The video aims to provide an intuitive understanding of these concepts, preparing viewers for practical applications in future lessons.

What is meant by minimizing the loss function in the context of training data?

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How do derivatives relate to the loss function in machine learning?

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In what way does the change in one variable affect the loss function according to the video?

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