Hyperparameters are parameters that are used to control the learning process in machine learning). They are different from other parameters, such as node weights, which are derived via training). Hyperparameters can be classified as model hyperparameters or algorithm hyperparameters). Model hyperparameters refer to the model selection task and cannot be inferred while fitting the machine to the training set, while algorithm hyperparameters have no influence on the performance of the model but affect the speed and quality of the learning process). Examples of model hyperparameters include the topology and size of a neural network, while examples of algorithm hyperparameters include the learning rate in optimization algorithms and the choice of optimization algorithm. Hyperparameters are set manually by the machine learning engineer before the learning algorithm begins training the model and cannot be changed during the training process. Hyperparameters directly control model structure, function, and performance, and choosing appropriate hyperparameter values is crucial for success. Some common hyperparameters include the learning rate, batch size, and number of hidden layers.