#Functional Programming

A curated collection of online talks from various fields, such as Software Development and Web Development, providing valuable insights from industry experts. The list is regularly updated with new content based on community suggestions through pull requests, fostering a collaborative approach. Explore diverse categories including Security, Test-Driven Development, and more for a broad learning experience tailored to professional interests.

Lean 4 offers resources for functional programming and theorem proving, including a quickstart guide, comprehensive tutorials, and installation documentation. It also provides instructions for contributing, updates, and answers to common questions, presenting a robust toolkit for enhancing programming and proving skills.

Discover detailed Swift programming techniques and server-side development through a structured video series. Learn about a well-organized codebase featuring core application operations, a unified styling library, and a server framework powered by NIO. Engage with video tours, extensive testing using snapshot methods, and interactive playgrounds. Access insights from Swift Summit 2017 and explore related open-source projects for practical enhancement of web and app development skills.

`samber/mo` leverages Go 1.18+ Generics for integrating monads and functional programming constructs into Go projects, supporting types like `Option[T]`, `Result[T]`, and `Either[A, B]`. Following Semantic Versioning, the library ensures no breaking changes before v2.0.0 and is easily installed using `go get github.com/samber/mo@v1`. Its sole dependency is the Go standard library, providing utilities like `Some` and `None` for managing optional values and results effectively.

FluentResults enhances .NET application operations by replacing exceptions with object-oriented error and success messages. It supports various .NET frameworks and contexts, enables multiple error storage, and integrates with FluentAssertions for advanced assertion techniques.

fklearn applies functional programming to streamline practical machine learning problem-solving, focusing on readiness, validation, and reproducibility of models. It facilitates alignment between production models and validated approaches, with straightforward installation via pip or from source. Designed for real-life application validation and detailed analysis, it ensures easy model reproducibility. Licensed under Apache 2.0, it is a dependable open-source framework for developers developing reliable machine learning solutions. Access extensive documentation and API references to integrate into projects efficiently.