Definition
Backpropagation, short for backward propagation of errors, is a method used in artificial neural networks to calculate the gradient of the loss function with respect to the weights. It works by propagating the error from the output layer back through the hidden layers to update weights using optimization algorithms like gradient descent. This iterative process allows the network to learn from its mistakes and improve prediction accuracy over time.
Summary
A widely used algorithm for training neural networks by calculating gradients to minimize error.
Key Concepts
- Gradient Descent
- Chain Rule
- Loss Function
- Weight Update
Use Cases
- Training deep neural networks
- Image classification tasks
- Speech recognition systems