Reference 2 stops to get here
Online Learning
Models that learn continuously from streaming data, updating incrementally as new data arrives.
Your route here
2 stops · basics first
- Dataset ✓ understood
A collection of data examples used for training, validating, or testing machine learning models.
- Training ✓ understood
The process of fitting a model to data by repeatedly measuring how wrong its outputs are and adjusting its parameters to reduce that error.
- Online Learning · you are here ✓ understood
Where it sits
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Training Continual Learning Learning new tasks sequentially without forgetting previously learned tasks, addressing catastrophic forgetting. Shipping AI Data Drift Changes in input data distribution over time that can degrade model performance in production. Shipping AI Model Retraining Periodically updating models with new data to maintain performance as distributions change. Training Stochastic Gradient Descent A variant of gradient descent that updates parameters using gradients computed on a single random training example at a time (though often used to refer to mini-batch gradient descent). Training Catastrophic Forgetting The tendency of neural networks to completely forget previously learned information when learning new tasks.
In the research
All papers →A paper that builds on Online Learning .
