awesome-machine-learning
A curated list of awesome Machine Learning frameworks, libraries and software.
https://github.com/josephmisiti/awesome-machine-learning
Last synced: 8 days ago
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C
[Tools](#tools-1)
- Darknet - Darknet is an open source neural network framework written in C and CUDA. It is fast, easy to install, and supports CPU and GPU computation.
- Recommender - A C library for product recommendations/suggestions using collaborative filtering (CF).
- neonrvm - neonrvm is an open source machine learning library based on RVM technique. It's written in C programming language and comes with Python programming language bindings.
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cONNXr
- An
ONNXruntime written in pure C (99) with zero dependencies focused on small embedded devices. Run inference on your machine learning models no matter which framework you train it with. Easy to install and compiles everywhere, even in very old devices. - libonnx - A lightweight, portable pure C99 onnx inference engine for embedded devices with hardware acceleration support.
- notorch - Neural networks framework in pure C: training and inference, no dependencies.
- qwen3.8-27b-in-c - A native C inference engine for running Qwen3.8-27B locally on a single laptop CPU, with direct GGUF loading and a tested 8 GB memory path.
- VoxRT - On-device streaming speech recognition runtime with C API for Linux (aarch64/x86_64). Based on NVIDIA NeMo FastConformer (80 ms cache-aware lookahead). Companion Silero VAD, wake-word, and 14-command KWS via same runtime.
C++
[Tools](#tools-1)
- N2D2 - CEA-List's CAD framework for designing and simulating Deep Neural Network, and building full DNN-based applications on embedded platforms
- CosmoEdge - A C++ edge video AI engine for RTSP ingestion, CV/VLM inference, visual pipeline orchestration, alarms, and event delivery on Sophon and Rockchip NPUs.
Go
[Tools](#tools-1)
- snowball - Snowball Stemmer for Go.
Lua
[Tools](#tools-1)
Python
General-Purpose Machine Learning
Ruby
General-Purpose Machine Learning
- Stemmer - Expose libstemmer_c to Ruby. [Deprecated]