What Is Optimization?

What Is Optimization?

Optimization is the process of adjusting a model’s internal weights to reduce loss and improve performance. It is the core mechanism that enables AI to learn.

Why Optimization Matters

• Improves accuracy: Ensures the model performs better over time.
• Drives learning: Without optimization, the model cannot learn patterns.
• Controls training speed: Some optimizers train faster or more stable than others.

Common Optimizers

• SGD (Stochastic Gradient Descent)
• Adam (very popular)
• RMSProp
• Adagrad

How Optimization Works

The optimizer uses the loss function to decide which direction to adjust the model’s weights, step by step, across many epochs.

Optimization FAQ

Is the optimizer used during inference?

No—only during training.

Do different optimizers give different results?

Yes—some converge faster or more accurately.