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<a name="cpp-general-purpose-machine-learning"></a>
#### General-Purpose Machine Learning
* [Nebullvm](https://github.com/nebuly-ai/nebullvm) - Easy-to-use library to boost AI inference leveraging multiple deep learning compilers. [DEEP LEARNING]
* [BanditLib](https://github.com/jkomiyama/banditlib) - A simple Multi-armed Bandit library. **[Deprecated]**
* [Caffe](https://github.com/BVLC/caffe) - A deep learning framework developed with cleanliness, readability, and speed in mind. [DEEP LEARNING]
* [CatBoost](https://github.com/catboost/catboost) - General purpose gradient boosting on decision trees library with categorical features support out of the box. It is easy to install, contains fast inference implementation and supports CPU and GPU (even multi-GPU) computation.
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* [mlpack](https://www.mlpack.org/) - A scalable C++ machine learning library.
* [MXNet](https://github.com/apache/incubator-mxnet) - Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Go, Javascript and more.
* [N2D2](https://github.com/CEA-LIST/N2D2) - CEA-List's CAD framework for designing and simulating Deep Neural Network, and building full DNN-based applications on embedded platforms
* [oneDNN](https://github.com/oneapi-src/oneDNN) - An open-source cross-platform performance library for deep learning applications.
* [ParaMonte](https://github.com/cdslaborg/paramonte) - A general-purpose library with C/C++ interface for Bayesian data analysis and visualization via serial/parallel Monte Carlo and MCMC simulations. Documentation can be found [here](https://www.cdslab.org/paramonte/).
* [proNet-core](https://github.com/cnclabs/proNet-core) - A general-purpose network embedding framework: pair-wise representations optimization Network Edit.
* [PyCaret](https://github.com/pycaret/pycaret) - An open-source, low-code machine learning library in Python that automates machine learning workflows.
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* [Spinning Up](https://spinningup.openai.com) - An educational resource designed to let anyone learn to become a skilled practitioner in deep reinforcement learning
* [Maze](https://github.com/enlite-ai/maze) - Application-oriented deep reinforcement learning framework addressing real-world decision problems.
* [RLlib](https://github.com/ray-project/ray) - RLlib is an industry level, highly scalable RL library for tf and torch, based on Ray. It's used by companies like Amazon and Microsoft to solve real-world decision making problems at scale.
* [DI-engine](https://github.com/opendilab/DI-engine) - DI-engine is a generalized Decision Intelligence engine. It supports most basic deep reinforcement learning (DRL) algorithms, such as DQN, PPO, SAC, and domain-specific algorithms like QMIX in multi-agent RL, GAIL in inverse RL, and RND in exploration problems.
<a name="ruby"></a>
## Ruby
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<a name="tools-misc"></a>
#### Misc
* [milvus](https://milvus.io) – Milvus is [open source](https://github.com/milvus-io/milvus) vector database for production AI, written in Go and C++, scalable and blazing fast for billions of embedding vectors.
* [milvus](https://milvus.io) – Milvus is [open source](https://github.com/milvus-io/milvus) vector database for production AI, written in Go and C++, scalable and blazing fast for billions of embedding vectors.
* [Weaviate](https://www.semi.technology/developers/weaviate/current/) – Weaviate is an [open source](https://github.com/semi-technologies/weaviate) vector search engine and vector database. Weaviate uses machine learning to vectorize and store data, and to find answers to natural language queries. With Weaviate you can also bring your custom ML models to production scale.
* [MLReef](https://about.mlreef.com/) - MLReef is an end-to-end development platform using the power of git to give structure and deep collaboration possibilities to the ML development process.
* [Pinecone](https://www.pinecone.io/) - Vector database for applications that require real-time, scalable vector embedding and similarity search.

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Add Squirrel https://github.com/merantix-momentum/squirrel-core