Training
Backpropagation
Algorithm for training neural networks by propagating errors backward
What is Backpropagation?
Backpropagation is the fundamental algorithm for training neural networks. It calculates the gradient of the loss function with respect to each weight by propagating error signals backward through the network, enabling efficient optimization using gradient descent.
Key Points
1
Calculates gradients efficiently
2
Uses chain rule of calculus
3
Enables deep network training
4
Foundation of modern deep learning
Practical Examples
Training feedforward networks
Optimizing deep architectures
Learning image features
Language model training