Term
Learning Rate
Definition
The learning rate in AI determines how much and how quickly a model changes its behavior based on errors it makes in predictions. It adjusts the sensitivity of the model to learn from its mistakes during training.
Where you’ll find it
In AI platforms, you will typically encounter the learning rate setting within the training or optimization settings of a model. This setting is part of the configuration process and is available across various AI tools and frameworks, regardless of the plan or version.
Common use cases
- Optimizing model performance: Adjust the learning rate to help the model learn efficiently without missing or overshooting the best solution.
- Improving training speed: Tweak the learning rate to find a balance that allows for quick training without sacrificing accuracy.
- Preventing training instability: Set a suitable learning rate to ensure the model does not become unstable or divergent during training.
Things to watch out for
- Overfitting: A high learning rate might cause the model to overfit, learning the training data too well and failing to generalize.
- Underfitting: A low learning rate might result in underfitting, where the model learns too slowly, capturing too little from the training data.
- Platform-specific defaults: Always check the default learning rate values, as they might not be optimal for all types of data or problems.
Related terms
- Hyperparameter Tuning
- Model Optimization
- Training Convergence
- Overfitting
- Underfitting