Parameters vs Hyperparameters

People confuse these all the time.

Parameters: learned by the model during training. (e.g., weights in a neural network, coefficients in regression).

Hyperparameters: set before training. They control how the model learns. (e.g., learning rate, number of layers, batch size).

✔️ Parameters = the student’s knowledge (changes as they study).
✔️ Hyperparameters = the teacher’s instructions (fixed rules of how to study).

Tuning hyperparameters is often the difference between a good model and a useless one.