Hyperparameters are the configuration settings used to structure the learning process in machine learning models. They are set prior to training a model and are not learned from the data.

• Neural Networks
• Deep Learning
• Gradient Descent optimization
• Support Vector Machines
• Decision Trees
• Bayesian Networks
• Random Forests
• K-Nearest Neighbors

In a neural network, the learning rate and regularization strength are examples of hyperparameters that can be adjusted before training to optimize performance.

• Designing the semantic data model