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@ -131,6 +131,7 @@ Further resources:
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- [Federated Learning](#python-federated-learning)
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- [Federated Learning](#python-federated-learning)
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- [Kaggle Competition Source Code](#python-kaggle-competition-source-code)
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- [Kaggle Competition Source Code](#python-kaggle-competition-source-code)
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- [Reinforcement Learning](#python-reinforcement-learning)
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- [Reinforcement Learning](#python-reinforcement-learning)
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- [Speech Recognition](#python-speech-recognition)
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- [Ruby](#ruby)
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- [Ruby](#ruby)
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- [Natural Language Processing](#ruby-natural-language-processing)
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- [Natural Language Processing](#ruby-natural-language-processing)
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- [General-Purpose Machine Learning](#ruby-general-purpose-machine-learning)
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- [General-Purpose Machine Learning](#ruby-general-purpose-machine-learning)
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@ -1277,6 +1278,7 @@ be
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* [Frouros](https://github.com/IFCA/frouros): Frouros is an open source Python library for drift detection in machine learning systems.
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* [Frouros](https://github.com/IFCA/frouros): Frouros is an open source Python library for drift detection in machine learning systems.
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* [CometML](https://github.com/comet-ml/comet-examples): The best-in-class MLOps platform with experiment tracking, model production monitoring, a model registry, and data lineage from training straight through to production.
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* [CometML](https://github.com/comet-ml/comet-examples): The best-in-class MLOps platform with experiment tracking, model production monitoring, a model registry, and data lineage from training straight through to production.
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* [Okrolearn](https://github.com/Okerew/okrolearn): A python machine learning library created to combine powefull data analasys feautures with tensors and machine learning components, while mantaining support for other libraries.
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* [Okrolearn](https://github.com/Okerew/okrolearn): A python machine learning library created to combine powefull data analasys feautures with tensors and machine learning components, while mantaining support for other libraries.
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* [Opik](https://github.com/comet-ml/opik): Evaluate, trace, test, and ship LLM applications across your dev and production lifecycles.
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<a name="python-data-analysis--data-visualization"></a>
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<a name="python-data-analysis--data-visualization"></a>
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#### Data Analysis / Data Visualization
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#### Data Analysis / Data Visualization
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@ -1474,6 +1476,10 @@ be
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* [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.
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* [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.
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* [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.
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* [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.
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<a name="python-speech-recognition"></a>
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#### Speech Recognition
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* [EspNet](https://github.com/espnet/espnet) - ESPnet is an end-to-end speech processing toolkit for tasks like speech recognition, translation, and enhancement, using PyTorch and Kaldi-style data processing.
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<a name="ruby"></a>
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<a name="ruby"></a>
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## Ruby
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## Ruby
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