Deep Learning - Deep Neural Network for Beginners Using Python - Chain Rule for Backpropagation

The video tutorial introduces the concept of the chain rule in calculus, explaining its application in neural networks for error calculation. It covers the process of forward and backpropagation, emphasizing the importance of storing neuron values for updating weights. The tutorial outlines the course structure, focusing on simple neural networks initially, with plans to explore deeper networks in future lessons.

What is the chain rule and how is it applied in the context of neural networks?

Explain the process of calculating the derivative of B with respect to X using the chain rule.

How do you compute the derivative of W11 with respect to the error in a neural network?

Discuss the relationship between forward propagation and backpropagation in neural networks.

What is the significance of storing neuron values during forward propagation?

Describe the steps involved in backpropagation and how partial derivatives are used.

What are the key differences between a simple neural network and a deep neural network?