Functional Programming
Definition of Functional Programming
Functional Programming: Functional programming is a style of programming in which the programmer focuses on functions instead of objects. In functional programming, functions are treated as first-class citizens, meaning they can be passed around and used like any other variable. Functional programming languages typically emphasize simplicity and purity, meaning that functions should be written in a way that is easy to understand and does not introduce any unnecessary complexity.
What is Functional Programming used for?
Functional Programming is a programming paradigm that emphasizes the application of functions to manipulate and transform data. It is an approach to software development that encourages the definition of all operations on data as functions, in contrast to imperative or object-oriented programming which relies heavily on statements and objects. Functional Programming has become increasingly popular in recent years, especially with the rise of machine learning and data science applications.
Functional Programming facilitates code reuse and code readability by relying on reusable functions rather than variables or objects. This allows a programmer to write code that more easily expresses their intent, making it easier for others to read and understand. It also allows developers to take advantage of higher order functions – such as map, reduce, filter – which can be used for efficient processing of large datasets or complex algorithms. In addition, it enables developers to develop programs using less code than traditional languages like Java or C++ and eliminates the need for lots of temporary variables which can lead to faster execution time with fewer bugs.
Furthermore, Functional Programming encourages immutable data structures and side effect free expressions which make it easier for developers to reason about their code without worrying about unexpected changes occurring elsewhere in the program due its stateful nature. This helps improve program reliability since any change made elsewhere does not affect this particular section of code as long as all parameters remain constant, allowing for better test coverage since each function only needs to be tested once rather than multiple times depending on variable values. All these features make Functional Programming a great choice when dealing with large datasets and complex algorithms in Machine Learning projects, where reliability is paramount.
