Model Training & Adaptation
Dropout
Temporarily leaving out randomly chosen parts during AI training so it depends less on particular connections.
Example
Different connections are temporarily left out while AI practices.
Why people use it
It helps a network avoid depending too strongly on particular internal paths while learning.
What you'll hear
“Make it learn without relying on the same parts every time.”
What this means for you
Check that the setting is suitable for the particular training setup.
Can you control it?
Developer-only
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
- Are parts switched off during normal use as well as training?
- Standard dropout is usually inactive during ordinary use; the finished system uses the learned connections with appropriate adjustments.
- Are the same parts left out on every training step?
- Usually not. The random selection changes during training.
- Can too much dropout make learning worse?
- Yes. Removing too much information can prevent useful patterns from being learned.