Directory
Published directory entries matching your search.
Oml is an OCaml math library that collects OCaml math and statistics functions. It aims for a descriptive, simple and typed approach to algorithms. A superset, Oml_full, includes C and Fortran dependencies such as Lacaml and LBFGS.
emel is a simple and functional machine learning library built for the Erlang ecosystem and implemented in Gleam. It turns data into functions and supports installing through rebar.config, mix.exs or gleam add. Implemented algorithms include linear regression, k nearest neighbors, decision tree, naive Bayes, k means, perceptron, logistic regression and neural network, along with mathematics, algebra, geometry and statistics.
Incanter is a Clojure-based, R-like platform for statistical computing and graphics. It provides charting and visualization, mathematical, statistical, matrix and linear algebra, and data manipulation functions. It can be used as a standalone interactive data analysis environment or embedded within other analytics systems as a modular suite of libraries.
NumPy is a Python library providing a multidimensional array object and routines for fast operations on arrays, including mathematical, logical, sorting, Fourier transforms, linear algebra and random simulation. It is open source under a BSD license and is described as the fundamental package for scientific computing in Python, with core code written in optimized C.
ParaMonte is a serial and parallel library of Monte Carlo routines for sampling mathematical density functions of arbitrary dimensions and machine learning algorithms for scientific inference. It is designed for interoperability with C, C++, Fortran, MATLAB and Python, and supports shared-memory OpenMP threads and MPI distributed communications.
The Hundred-Page Language Models Book by Andriy Burkov is a concise hands-on guide covering machine learning basics through advanced transformer architectures and large language models. It explains AI fundamentals, text representation, count-based models, recurrent neural networks, and transformers, with mathematical foundations and Python code examples. The book targets ML practitioners and engineers building text-based applications.
