Hyperparameter

A hyperparameter is a setting that controls how an AI model learns during training. It is similar to adjusting the knobs on a machine to get the best performance. Examples of hyperparameters include how fast the model learns, how many layers it has, or how complex each layer is. Choosing the right hyperparameters is crucial for building an effective model.

Unlike parameters, which the AI learns from data on its own, hyperparameters are set by the developers ahead of time. Finding the right combination often involves testing and tweaking. It is a bit like baking a cake. You might need to experiment with temperature, baking time, or ingredient ratios to get the perfect result.