Model Training & Adaptation
Optimizer
The algorithm that uses gradients to update a model's parameters.
Example
Adam adjusts model weights using gradients and a history of recent updates.
Why it matters
It helps turn calculated learning signals into useful changes to the system.
You'll hear it
“The same examples learned differently with another optimizer.”
Can you control it?
Set by the team
The people building or running the AI choose this setup. An everyday user generally needs their help to change how this part works.
Common questions
- Does one optimizer work best for every task?
- No. The best choice depends on the system, examples and available resources.
- Can an optimizer compensate for bad teaching answers?
- Not reliably. Improving the chosen score can still reinforce mistakes in the material.
- Does changing the optimizer change the goal automatically?
- No. The method of adjustment and the target being pursued are separate choices.