Mini toolsDefinition
Algorithm for efficiently computing gradients in neural networks by propagating errors backward through the network.
Detailed Explanation
Backpropagation efficiently computes gradients using the chain rule of calculus, propagating error signals backward through the network. It enables efficient training of deep neural networks by calculating how each parameter contributes to the final error. The algorithm includes forward pass (prediction) and backward pass (gradient computation) phases.
Use Cases
Training deep neural networks across all applications
