Search Results for "learning language"

Servant provides a type-level domain-specific language (DSL) in Haskell for describing web APIs. From a single API specification, developers can derive server implementations, client libraries, documentation, and more—ensuring consistency and type safety across the stack. We have a tutorial that introduces the core features of servant. After this article, you should be able to write your first server web services, learning the rest from the haddocks' examples.

Compile to readable JavaScript and reuse existing JavaScript code easily. An extensive collection of libraries for development of web applications, web servers, apps and more. Excellent tooling and editor support with instant rebuilds. An active community with many learning resources. Build real-world applications using functional techniques and expressive types, such as: Algebraic data types and pattern matching. Row polymorphism and extensible records. Higher kinded types and type classes...

The HaskellR project provides an environment for efficiently processing data using Haskell or R code, interchangeably. HaskellR allows Haskell functions to seamlessly call R functions and vice versa. It provides the Haskell programmer with the full breadth of existing R libraries and extensions for numerical computation, statistical analysis and machine learning. Optionally, pass in the --nix flag to all commands if you have the Nix package manager installed. Nix can populate a local build...

The tensorflow-haskell package provides Haskell-language bindings for TensorFlow, giving Haskell developers the ability to build and run computation graphs, machine learning models, and leverage TensorFlow's ecosystem—though it is not an official Google release. As an expedient we use docker for building. Once you have docker working, the following commands will compile and run the tests.

HLearn is a Haskell-based machine learning library focused on composability, algebraic structure, and performance. It provides a functional approach to building machine learning algorithms by leveraging algebraic properties such as monoids and groups. This allows for parallel, incremental, and distributed computation in a mathematically consistent way. HLearn aims to provide implementations of common algorithms like k-means, naive Bayes, and others while maintaining the expressiveness and safety of the Haskell language.