A curated list of awesome C++ (or C) frameworks, libraries, resources, and shiny things. Inspired by awesome-... stuff.
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A curated list of awesome C++ (or C) frameworks, libraries, resources, and shiny things. Inspired by awesome-... stuff.
🚀 Awesome list of open source applications for macOS. https://t.me/s/opensourcemacosapps
Collection of various algorithms in mathematics, machine learning, computer science and physics implemented in C++ for educational purposes.
A brief computer graphics / rendering course
Modern C++ Programming Course (C++03/11/14/17/20/23/26)
A FREE comprehensive reverse engineering tutorial covering x86, x64, 32-bit/64-bit ARM, 8-bit AVR and 32-bit RISC-V architectures.
mal - Make a Lisp
Browser-based frontend to gdb (gnu debugger). Add breakpoints, view the stack, visualize data structures, and more in C, C++, Go, Rust, and Fortran. Run gdbgui from the terminal and a new tab will open in your browser.
180+ Algorithm & Data Structure Problems using C++
The public CGAL repository, see the README below
A curated list of awesome header-only C++ libraries
C++ implementation of the Python Numpy library
A fast and flexible implementation of Rigid Body Dynamics algorithms and their analytical derivatives
Embedded Template Library
Functional Programming Library for C++. Write concise and readable C++ code.
C++ implementation of a fast hash map and hash set using robin hood hashing
C++ implementation of a fast and memory efficient HAT-trie
C++ implementation of a fast hash map and hash set using hopscotch hashing
C++ hash map and hash set which preserve the order of insertion
A C++ implementation of timsort
C++ Bitmap Library https://www.partow.net/programming/bitmap/index.html
A fast and flexible c++ template class for tree data structures
Support vector machines (SVMs) and related kernel-based learning algorithms are a well-known class of machine learning algorithms, for non-parametric classification and regression. liquidSVM is an implementation of SVMs whose key features are: fully integrated hyper-parameter selection, extreme speed on both small and large data sets, full flexibility for experts, and inclusion of a variety of different learning scenarios: multi-class classification, ROC, and Neyman-Pearson learning, and least-squares, quantile, and expectile regression.