Collection of various algorithms in mathematics, machine learning, computer science, physics, etc implemented in C for educational purposes.

The repository is a collection of open-source implementations of a variety of algorithms implemented in C and licensed under GPLv3 License. The algorithms span a variety of topics from computer science, mathematics and statistics, data science, machine learning, engineering, etc.. The implementations and their associated documentations are meant to provide a learning resource for educators and students. Hence, one may find more than one implementation for the same objective but using different algorithm strategies and optimizations. The repository provides implementations of various algorithms in one of the most fundamental general purpose languages - C. Well documented source code with detailed explanations provide a valuable resource for educators and students alike. Each source code is atomic using standard C library libc and no external libraries are required for their compilation and execution. Thus the fundamentals of the algorithms can be studied in much depth. Source codes are compiled and tested for every commit on the latest versions of three major operating systems viz., Windows, MacOS and Ubuntu (Linux) using MSVC 16 2019, AppleClang 11.0 and GNU 7.5.0 respectively. Strict adherence to C11 standard ensures portability of code to embedded systems as well like ESP32, ARM Cortex, etc. with little to no changes. Self-checks within programs ensure correct implementations with confidence. Modular implementations and OpenSource licensing enable the functions to be utilized conveniently in other applications. 

A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.

Website | Documentation | Tutorials | Installation | Release Notes. CatBoost is a machine learning method based on gradient boosting over decision trees. 

Greenplum Database - Massively Parallel PostgreSQL for Analytics. An open-source massively parallel data platform for analytics, machine learning and AI.

A Greenplum cluster consists of a coordinator server, and multiple segment servers. All user data resides in the segments, the coordinator contains only metadata. The coordinator server, and all the segments, share the same schema. Users always connect to the coordinator server, which divides up the query into fragments that are executed in the segments, and collects the results. More information can be found on the project website. 

An Embedded Computer Vision & Machine Learning Library (CPU Optimized & IoT Capable)

An Embedded Computer Vision & Machine Learning Library (CPU Optimized & IoT Capable)Built for real world and real-time applications. State-of-the-art, CPU optimized deep-neural networks including the brand new, exclusive RealNets architecture. Patent-free, advanced computer vision algorithms. Support major image format. Simple, clean and easy to use API. Brings deep learning on limited computational resource, embedded systems and IoT devices. Easy interpolatable with OpenCV or any other proprietary API. Pre-trained models available for most architectures. CPU capable, RealNets model training. Production ready, cross-platform, high quality source code. SOD is dependency free, written in C, compile and run unmodified on virtually any platform &amp; architecture with a decent C compiler. Amalgamated - All SOD source files are combined into a single C file (sod.c) for easy deployment. Open-source, actively developed &amp; maintained product. Developer friendly support channels. 

implement AlexNet with C    /  convolutional nerual network  /    machine learning  /  computer vision

This project is an unofficial implementation of AlexNet, using C Program Language Without Any 3rd Library, according to the paper "ImageNet Classification with Deep Convolutional Neural Networks" by Alex Krizhevsky,et al. Effective matrix multiply, w.r.t L1/L2 cache img2col implementation of convolutional layer Multi-thread CPU Apply MT in operator's forward computation and backward computation to boost speed Efficient Memory Usage &amp; Minimal Memory Occupation Memory is allocated on demand for efficient memory usage. If an array isn't necessary for computations afterward, it's memory will be freed instantly to minimize memory occupation. Modular layer Define each layer seperately 

Portable C++ library for signal processing and machine learning inferencing