josephmisiti/awesome-machine-learning
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* [stanford-corenlp-python](https://github.com/dasmith/stanford-corenlp-python) - Python wrapper for [Stanford CoreNLP](https://github.com/stanfordnlp/CoreNLP) **[Deprecated]**
* [CLTK](https://github.com/cltk/cltk) - The Classical Language Toolkit.
* [Rasa](https://github.com/RasaHQ/rasa) - A "machine learning framework to automate text-and voice-based conversations."
* [yase](https://github.com/PPACI/yase) - Transcode sentence (or other sequence) to list of word vector .
* [yase](https://github.com/PPACI/yase) - Transcode sentence (or other sequence) to list of word vector.
* [Polyglot](https://github.com/aboSamoor/polyglot) - Multilingual text (NLP) processing toolkit.
* [DrQA](https://github.com/facebookresearch/DrQA) - Reading Wikipedia to answer open-domain questions.
* [Dedupe](https://github.com/dedupeio/dedupe) - A python library for accurate and scalable fuzzy matching, record deduplication and entity-resolution.
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<a name="tools-misc"></a>
#### Misc
* [Wallaroo.AI](https://wallaroo.ai/) - Production AI plaftorm for deploying, managing, and observing any model at scale across any envirorment from cloud to edge. Let's you go from python notebook to inferencing in minutes.
* [Wallaroo.AI](https://wallaroo.ai/) - Production AI plaftorm for deploying, managing, and observing any model at scale across any environment from cloud to edge. Let's go from python notebook to inferencing in minutes.
* [Infinity](https://github.com/infiniflow/infinity) - The AI-native database built for LLM applications, providing incredibly fast vector and full-text search. Developed using C++20
* [Synthical](https://synthical.com) - AI-powered collaborative research environment. You can use it to get recommendations of articles based on reading history, simplify papers, find out what articles are trending, search articles by meaning (not just keywords), create and share folders of articles, see lists of articles from specific companies and universities, and add highlights.
* [Humanloop](https://humanloop.com) – Humanloop is a platform for prompt experimentation, finetuning models for better performance, cost optimization, and collecting model generated data and user feedback.
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* [MLFlow](https://mlflow.org/) - platform to manage the ML lifecycle, including experimentation, reproducibility and deployment. Framework and language agnostic, take a look at all the built-in integrations.
* [Weights & Biases](https://www.wandb.com/) - Machine learning experiment tracking, dataset versioning, hyperparameter search, visualization, and collaboration
* More tools to improve the ML lifecycle: [Catalyst](https://github.com/catalyst-team/catalyst), [PachydermIO](https://www.pachyderm.io/). The following are GitHub-alike and targeting teams [Weights & Biases](https://www.wandb.com/), [Neptune.ai](https://neptune.ai/), [Comet.ml](https://www.comet.ml/), [Valohai.ai](https://valohai.com/), [DAGsHub](https://DAGsHub.com/).
* [Arize AI](https://www.arize.com) - Model validaiton and performance monitoring, drift detection, explainability, visualization across structured and unstructured data
* [Arize AI](https://www.arize.com) - Model validation and performance monitoring, drift detection, explainability, visualization across structured and unstructured data
* [MachineLearningWithTensorFlow2ed](https://www.manning.com/books/machine-learning-with-tensorflow-second-edition) - a book on general purpose machine learning techniques regression, classification, unsupervised clustering, reinforcement learning, auto encoders, convolutional neural networks, RNNs, LSTMs, using TensorFlow 1.14.1.
* [m2cgen](https://github.com/BayesWitnesses/m2cgen) - A tool that allows the conversion of ML models into native code (Java, C, Python, Go, JavaScript, Visual Basic, C#, R, PowerShell, PHP, Dart) with zero dependencies.
* [CML](https://github.com/iterative/cml) - A library for doing continuous integration with ML projects. Use GitHub Actions & GitLab CI to train and evaluate models in production like environments and automatically generate visual reports with metrics and graphs in pull/merge requests. Framework & language agnostic.