What is a Hyperparameter?

Detailed Deep Dive

A hyperparameter is a configuration variable whose value is set before the training process begins, rather than being learned by the model during training. Examples include learning rate, batch size, number of hidden layers, and dropout rate. Finding the optimal configuration of hyperparameters is a critical step in training machine learning models, often performed using search strategies like grid search, random search, or Bayesian optimization.