DL Foundations
Backpropagation — How Neural Networks Actually Learn
Backpropagation is the algorithm that trains all neural networks. It efficiently computes the gradient of the loss with respect to every parameter using the chain rule, enabling gradient descent optimization.
- Forward Pass — Compute outputs layer by layer while storing intermediate values
- Backward Pass — Propagate error signals backward to compute parameter gradients
- Efficient Gradient Computation — Same complexity as forward pass, reusing intermediate computations
Backpropagation Algorithm — Forward Pass, Backward Pass and Computational Graphs
Backpropagation is the algorithm that enables neural networks to learn from data. It efficiently computes the gradient of the loss with respect to every parameter using the chain rule.
What Is Backpropagation?
Computational Graphs
Forward Pass
Backward Pass
Gradient Flow Through Layers
PyTorch Autograd
Practical Considerations
Summary
- Backpropagation efficiently computes gradients using the chain rule
- The forward pass computes outputs and stores intermediates; the backward pass computes gradients
- Computational graphs represent the sequence of operations for automatic differentiation
- Vanishing/exploding gradients are fundamental challenges solved by architectural innovations
- PyTorch autograd implements reverse-mode automatic differentiation
Next: Activation Functions