davisking/dlib

A toolkit for making real world machine learning and data analysis applications in C++

What it solves

Dlib is a C++ toolkit designed to provide a comprehensive set of machine learning algorithms and tools for developers to build complex software that solves real-world problems.

How it works

It provides a modern C++ library with a corresponding Python API, allowing developers to integrate machine learning capabilities directly into their applications. The library can be optimized for performance using AVX instructions on supported CPUs.

Who it’s for

Software engineers and researchers who need a high-performance machine learning toolkit for C++ and Python environments.

Highlights

  • Modern C++ toolkit with a Python API.
  • Support for AVX instructions to increase execution speed.
  • Compatible with C++17 and CMake.
  • Licensed under the Boost Software License, allowing for use in closed-source commercial software.

Related

  • Project
    dmlc/dmlc-core

    A backbone C++ library that provides the essential building blocks for creating efficient and scalable distributed machine learning systems.

  • Project
    uxlfoundation/oneDAL

    A C++ and DPC++ library that provides hardware-accelerated machine learning routines for tabular data across CPUs, GPUs, and distributed systems.

  • Project
    Le0nX/ModernRoboticsCpp

    A C++ library accompanying the Modern Robotics textbook, providing educational implementations of robotics mechanics, planning, and control algorithms.

  • Project
    esa/pagmo2

    A C++ scientific library for massively parallel global multiobjective optimization providing a unified interface for algorithms and problems.