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Dlib is a modern C++ toolkit containing machine learning algorithms and tools for creating complex software in C++ to solve real world problems.
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•Contains machine learning algorithms and tools in order of creating complex software in C++ for solving real world problems •Provides complete and precise documentation for every class and function •High quality portable code •Graphical model inference algorithms
Small (<50 employees), Medium (50 to 1000 Enterprise (>1001 employees)
What is best?
•Contains machine learning algorithms and tools in order of creating complex software in C++ for solving real world problems •Provides complete and precise documentation for every class and function •High quality portable code
What are the benefits?
• Documentation for every class and function • Debugging modes that check documented preconditions for functions • Good unit test coverage • No other packages required to use the library • No installation or configuration step needed
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It is used in both industry and academia in a wide range of domains including robotics, embedded devices, mobile phones, and large high performance computing environments.
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Dlib is a modern C++ toolkit which contains machine learning algorithms and tools in order of creating complex software in C++ for solving real world problems. It is used in a wide range of domains including robotics, embedded devices, mobile phones, and large high performance computing environments.
It is free of any charges which mean that users can use it in any app. Major features of Dlib is: documentation – it provides complete and precise documentation for every class and function, lots of example programs are provided; high quality portable code – good unit test coverage, tested on MS Windows, Linux, and Mac OS X systems, but it should work on any POSIX system and has been used on Solaris, HPUX, and the BSDs, no installation needed before using the library, all operating system specific code is isolated inside the OS abstraction layers; machine learning algorithms – deep learning.
Conventional SMO based Support Vector Machines for classification and regression, reduced-rank methods for large-scale classification and regression, general purpose multiclass classification tools, a Multiclass SVM, a tool for solving the optimization problem associated with structural support vector machines etc.
Numerical algorithms – a fast matrix object implemented using the expression templates technique and capable of using BLAS and LAPACK libraries when available, numerous linear algebra and mathematical operations are defined for the matrix object such as the singular value decomposition, transpose, trig functions, general purpose unconstrained non-linear optimization algorithms using the conjugate gradient, BFGS, and L-BFGS techniques; graphical model inference algorithms; image processing – routines for reading and writing common image formats, automatic color space conversion between various pixel types, common image operations such as edge finding and morphological operations; threading; networking; testing and many others.
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