Python for Deep Learning - Build Neural Networks in Python - Feed-Forward and Back Propagation Networks

The video tutorial explains two key methods of signal propagation in neural networks: feedforward and backpropagation. The feedforward network (FFNN) allows information to move in one direction, from input to output nodes, without forming cycles. In contrast, backpropagation is used when the desired output is not achieved, allowing the network to adjust weights by moving backward through the network. This process helps the network learn and improve its accuracy.

What is the difference between feedforward and backpropagation networks?

Explain how a network learns and adjusts weights in backpropagation.