Object-oriented design in deep learning emphasizes high modularity by structuring systems into distinct classes that manage their own data and interactions. This design allows individual components—such as the optimizer, the model architecture, or the dataset—to be independently replaced or updated without altering the entire computational pipeline. Such modularity yields significant benefits in terms of code reuse, conciseness, and simplicity across diverse machine learning projects.