Gradient
Direction and magnitude of steepest increase in loss function with respect to parameters.
In italiano: GradienteGradients indicate how to adjust weights to reduce loss. Computed via backpropagation. Move in opposite direction (gradient descent).
Examples
- Weight gradient: -0.5
- Gradient flow through network
- Vanishing gradients