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Optimizer
Data / Training / Evaluation
Algorithm adjusting parameters to minimize loss.
Optimizers update model parameters during training to reduce loss based on gradient information.
- Examples: SGD, Adam, RMSprop.
- Goal: Efficient, stable, and fast convergence.