100 Facts About Hyperparameters

Photo by Google DeepMind on Unsplash

Here’s a list of 100 facts about hyperparameters:

1. Hyperparameters are external configurations that guide the training process of machine learning models.
2. They are distinct from parameters, which are internal variables learned by the model during training.
3. Examples of hyperparameters include learning rate, regularization strength, and the number of hidden layers in a neural network.
4. Hyperparameters are set before the training process begins and are not learned from the data.
5. Proper tuning of hyperparameters is crucial for achieving optimal model performance.
6. Grid search and random search are common techniques for hyperparameter tuning.
7. Grid search exhaustively tests a predefined set of hyperparameter combinations.
8. Random search samples hyperparameter values randomly from specified ranges.
9. Hyperparameter tuning aims to find the best combination that minimizes a chosen evaluation metric.
10. Learning rate controls the step size during gradient descent optimization.
11. Regularization strength penalizes complex models to prevent overfitting.
12. The number of hidden layers and units in a neural network is a critical…