Backpropagation calculus | Deep learning, chapter 4

The video tutorial provides an intuitive walkthrough of the backpropagation algorithm, focusing on its application in neural networks. It begins with a simple network example to explain the sensitivity of the cost function to weights and biases. The tutorial delves into the calculus involved, particularly the chain rule, to compute derivatives. It further explores how these derivatives form the gradient vector, which is crucial for minimizing the cost function through backpropagation. The tutorial emphasizes understanding the mathematical concepts and their implications in machine learning.

What new insight or understanding did you gain from this video?