https://www.cs.utah.edu/~germain/PPS/Topics/oop.html

University of Utah OOP Definition

This reference guide by Carl Erickson provides a comprehensive overview of Object-Oriented Programming (OOP) principles and design methodologies. It addresses the management of software complexity through foundational concepts such as abstraction, encapsulation, inheritance, and polymorphism. The text bridges theory and practice using C++ and Java examples, while detailing the development process through UML notation, CRC cards, and various analysis techniques. Additionally, it explores advanced topics like concurrency, persistence, and design pitfalls. The document concludes by establishing metrics for evaluating code quality, emphasizing low coupling and high cohesion to create robust, maintainable software architectures.

Object-Oriented Programming by Carl Erickson

University of Michigan - Ann Arbor

Object-Oriented Programming (OOP) is a programming paradigm that is based on the use of objects. Objects are reproducible models which contain various attributes and functions. The OOP paradigm is the most common paradigm used in industry and for education.

Object-Oriented Programming

The key components that are present in all OOP languages are:
- Abstraction
- Encapsulation
- Inheritance
- Polymorphism

OOP General Concepts

Object-Oriented Programming References

There are many languages that follow the OOP paradigm either partially or completely. Some of these languages include:
- Rust
- C++
- Python
- Go
- Java
- Javascript
- Php

Object-Oriented Programming (OOP) Languages

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.

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