What is Backpropagation in Deep Learning and how does it actually compute gradients?

Backpropagation is an efficient algorithm for computing gradients of a neural network’s loss with respect to all parameters using the chain rule of calculus.

Backpropagation is the backbone of modern deep learning training. It enables neural networks with millions or billions of parameters to be optimized efficiently.

Core idea: Instead of computing derivatives independently for each parameter (which would be extremely expensive), backprop reuses intermediate computations using dynamic programming over the computational graph.

Key concept: Computational Graph