Log Loss

Average of squared residuals; common regression objective.

Iterative method that updates parameters in the direction of negative gradient to minimize loss.

Uses an exponential moving average of gradients to speed convergence and reduce oscillation.

Popular optimizer combining momentum and per-parameter adaptive step sizes via first/second moment estimates.

Configuration choices not learned directly (or not typically learned) that govern training or architecture.

A scalar measure optimized during training, typically expected loss over data, sometimes with regularization terms.

Minimizing average loss on training data; can overfit when data is limited or biased.