Gradients estimate how the loss changes with each parameter, and the optimizer applies a scaled step in the opposite direction. Training repeats this process over batches until a stopping condition is reached.
Gradient descent is an optimization method that updates parameters in the direction that reduces a differentiable objective.
Gradients estimate how the loss changes with each parameter, and the optimizer applies a scaled step in the opposite direction. Training repeats this process over batches until a stopping condition is reached.