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General purpose GPU compute framework built on Vulkan to support 1000s of cross vendor graphics cards (AMD, Qualcomm, NVIDIA & friends). Blazing fast, mobile-enabled, asynchronous and optimized for advanced GPU data processing usecases. Backed by the Linux Foundation.
- vulkan
- vulkan-compute
- vulkan-example
- vulkan-tutorial
C++ image processing and machine learning library with using of SIMD: SSE, AVX, AVX-512, AMX for x86/x64, NEON, SVE for ARM, HVX for Hexagon
- simd
- avx
- neon
- image-processing
A lightweight header-only library for using Keras (TensorFlow) models in C++.
A uniform interface to run deep learning models from multiple frameworks
🏅State-of-the-art learned data structure that enables fast lookup, predecessor, range searches and updates in arrays of billions of items using orders of magnitude less space than traditional indexes
Header-Only C++ Library for Graph Representation and Algorithms
- search-algorithm
- dijkstra-algorithm
- graph
- graph-algorithms
captcha break based on opencv2, tesseract-ocr and some machine learning algorithm.
- machine-learning
- captcha
- opencv
- captcha-break
ThunderGBM: Fast GBDTs and Random Forests on GPUs
Framework and Library for Distributed Online Machine Learning
- machine-learning
- c-plus-plus
- ml
- distributed
oneAPI Data Analytics Library (oneDAL)
- onedal
- data-analysis
- oneapi
- machine-learning
A lightweight C++ machine learning library for embedded electronics and robotics.
Ruby language bindings for LIBSVM
- libsvm
- ruby-bindings
- ruby
- ruby-language-bindings
Cancer Imaging Phenomics Toolkit (CaPTk) is a software platform to perform image analysis and predictive modeling tasks. Documentation: https://cbica.github.io/CaPTk
- cancer-imaging-research
- radiomics
- radiomics-features
- machine-learning
⚡ Open-source software for deep learning-based digital pathology
- deep-learning
- machine-learning
- digital-pathology
- computational-pathology
Serverless ML Framework
The repository host gives no description.
- rubyml
- machine-learning
- ruby
- ml
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.
- svm
- regression
- classification
- machine-learning
Prototype of an intelligent safety system for detecting driver drowsiness
- cpp
- cuda
- cudnn
- driver-drowsiness-detector
A Ruby interface to the Timbl machine-learning library
Header-only C++11 library using OpenCV for high-throughput image-based plant phenotyping
- plant-phenotyping
- opencv
- charuco
- color-correction