Term
Hyperparameter (hī-pər-ˈpär-əm-ē-tər)
Definition
Hyperparameters are settings that shape how an AI model learns from data. They include options like learning rate, which affects how quickly a model adapts, and batch size, which determines how many pieces of data the model learns from at once.
Where you’ll find it
In AI platforms, hyperparameters are set up before training a model. You’ll find these settings in the configuration or training section of the AI tool. They are crucial across various models and frameworks and usually remain unchanged once training begins.
Common use cases
- Setting the learning rate to control how fast the AI learns patterns.
- Adjusting the batch size to manage memory usage and training speed.
- Fine-tuning model performance by experimenting with different hyperparameter values.
Things to watch out for
- Setting hyperparameters too high or too low can lead to poor model performance.
- It may be tempting to use default settings, but these might not be optimal for specific tasks.
- Some hyperparameters, like learning rates, may need adjustment as new data is introduced or as models change.
Related Terms
- Model Training
- Batch Processing
- Learning Rate
- Neural Network
- Model Optimization