Backpropagation
The primary algorithm used to train neural networks by calculating gradients and adjusting weights to minimise errors. It propagates error signals backward through the network.
In-Depth Explanation
Backpropagation (backward propagation of errors) is the fundamental algorithm for training neural networks. It efficiently calculates how much each weight in the network contributed to the error, enabling targeted adjustments.
How backpropagation works:
- Forward pass: Input propagates through network to output
- Loss calculation: Compare output to target, compute error
- Backward pass: Propagate error gradients back through layers
- Weight update: Adjust weights to reduce error
Key concepts:
- Gradient: Direction and magnitude of steepest increase
- Chain rule: Calculates gradients through composed functions
- Learning rate: Scales gradient updates
- Loss function: Measures prediction error
Why backpropagation matters:
- Enables training of deep networks
- Computationally efficient (linear in network size)
- Foundation of all modern deep learning
- Makes neural networks practical
Without backpropagation, training the billion-parameter models we use today would be impossible.
Business Context
Understanding backpropagation helps you appreciate why training custom models requires significant compute resources and why fine-tuning is often preferred.
How Clever Ops Uses This
While you don't need to implement backpropagation, understanding it helps us explain training trade-offs and resource requirements to Australian business clients.
Example Use Case
"During model training, backpropagation calculates how much each weight contributed to errors and adjusts them accordingly."