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 is the main goal of understanding the back propagation algorithm in machine learning?

How does the cost function relate to the weights and biases in a neural network?

How do small changes in weights affect the cost function according to the back propagation algorithm?

Explain the significance of the chain rule in the context of back propagation.

What does the derivative of the cost function with respect to the activation of the last layer indicate?

How is the cost function averaged across multiple training examples in back propagation?

What is the relationship between the derivatives of the cost function and the gradient vector?

Describe how the sensitivity of the cost function to weights and biases is determined.

In what way does the complexity of a neural network increase when adding more neurons to each layer?

What role does the activation function play in the back propagation process?