In deep learning, models may be categorized by the relationships they capture within data, which guides their application.

• Simple models handle independent data points, suited for tasks like basic classification and regression in tabular datasets.
• Sequential models process data with ordered dependencies, commonly used in applications like time series forecasting, natural language processing, or image analysis, where data follows a spatial or temporal sequence.
• Graph models address irregular but relational data, where dependencies are based on connectivity rather than order, making them applicable to scenarios like social network analysis, molecular modeling, or recommendation systems.

This taxonomy helps in a higher view, how to select the right model based on how data is structured in real-world applications.