What is Hyperparameter

A hyperparameter is a crucial concept in machine learning and deep learning. It refers to the parameters that are set before the model training process and are not learned from the training data. Instead, these parameters need to be manually specified. The choice of hyperparameters directly affects the model's performance and outcomes, making careful selection essential.

Common types of hyperparameters include learning rate, batch size, regularization parameters, and the depth and width of the network structure. Choosing the right hyperparameters can help the model better learn the data features, thereby improving its generalization ability on new data. The selection process often involves techniques such as cross-validation to ensure that the chosen parameters effectively enhance model performance.

However, the process of setting hyperparameters can be challenging and typically requires experience and extensive experimentation. In recent years, automated hyperparameter optimization techniques, such as Bayesian optimization, have emerged to reduce the time and effort involved in manual tuning. These methods intelligently search the hyperparameter space to find optimal combinations.

Looking ahead, as machine learning technologies continue to advance, hyperparameter optimization methods are expected to become more intelligent and automated, potentially leading to fully automated model training processes.