Optimizer
Definition
An optimizer changes parameters to improve an objective function.
During model training, it uses gradients to update weights, for example. Methods like SGD, Adam, or Nadam differ in step calculation.
Simply explained
A navigation method decides how the next step downhill to a smaller error looks.
Why it matters in practice
The choice influences training progress, computational effort, and stability.
Example from business
Hypothetical example: A team compares two optimizers with identical data splits.
Limits and pitfalls
- A small training error does not prove good generalization. Learning rate and other settings remain important.
Origin & history
Optimizers originate from numerical mathematics and are central components of ML training.
Use in mid-sized companies
- SMEs need the selection mainly for their own model training or expert evaluation of such projects.
Frequently asked questions
Is the optimizer the model?
No, it is the method that adjusts model parameters.
Sources
- Optimizers — · Keras: Optimizers —
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