https://github.com/Quantmetry/awesome_quantmetry

A list of repositories commonly used @ Quantmetry
https://github.com/Quantmetry/awesome_quantmetry

List: awesome_quantmetry

data-engineering machine-learning pioneers statistics

Last synced: about 1 month ago
JSON representation

A list of repositories commonly used @ Quantmetry

• Host: GitHub
• URL: https://github.com/Quantmetry/awesome_quantmetry
• Owner: Quantmetry
• Created: 2019-02-01T16:57:38.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2019-07-03T07:39:13.000Z (about 5 years ago)
• Last Synced: 2024-05-23T07:38:59.336Z (4 months ago)
• Topics: data-engineering, machine-learning, pioneers, statistics
• Homepage: https://quantmetry.com
• Size: 11.7 KB
• Stars: 14
• Watchers: 4
• Forks: 2
• Open Issues: 3
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

• ultimate-awesome - awesome_quantmetry - A list of repositories commonly used @ Quantmetry. (Other Lists / PowerShell Lists)

README

        
# awesome_quantmetry

![alt text][qm-contrib-head]

**A list of repositories commonly used at [Quantmetry](https://quantmetry.com)**

## Statistics / Machine Learning building blocks

* [scikit-learn](https://github.com/scikit-learn/scikit-learn)

* [statsmodels](https://github.com/statsmodels/statsmodels)

* [imbalanced-learn](https://github.com/scikit-learn-contrib/imbalanced-learn) (scikit-contrib)

* [keras](https://github.com/keras-team/keras)

## Interpretability / Explainable AI

* [SHAP](https://github.com/slundberg/shap)

* [skope-rules](https://github.com/scikit-learn-contrib/skope-rules) (scikit-contrib) ![alt text][qm-contrib]

* [Quantmetry intelligibility resources](https://github.com/Quantmetry/resources-intelligibility)

* [JP-Hall ML-Interpretability awesome list](https://github.com/jphall663/awesome-machine-learning-interpretability)

## Natural Language Processing

* [spaCy](https://github.com/explosion/spaCy)

* [NLTK](https://github.com/nltk/nltk)

* [gensim](https://github.com/rare-technologies/gensim)

* [pyLDAvis](https://github.com/bmabey/pyLDAvis)

* [melusine](https://github.com/MAIF/melusine) ![alt text][qm-contrib]

* [Mozilla's implementation of Baidu's DeepSpeech](https://github.com/mozilla/DeepSpeech)

## Computer Vision

* [OpenCV](https://github.com/opencv/opencv)

* [scikit-image](https://github.com/scikit-image/scikit-image)

* [retinanet](https://github.com/fizyr/keras-retinanet)

* [OpenCV](https://github.com/opencv/opencv)

* [MaskRCNN](https://github.com/matterport/Mask_RCNN)

## Time Series

* [tsfresh](https://github.com/blue-yonder/tsfresh)

* [Facebook Prophet](https://github.com/facebook/prophet)

* [statsmodels](https://github.com/statsmodels/statsmodels)

* [scikit-survival](https://github.com/sebp/scikit-survival)

## Data Engineering / deployment

* [Airflow](https://github.com/apache/airflow)

* [PySpark](https://github.com/apache/spark/tree/master/python/pyspark)

* [kafka-confluent](https://github.com/confluentinc/confluent-kafka-python)

* [pipeasy-spark](https://github.com/Quantmetry/pipeasy-spark) ![alt text][qm-contrib]

## Web/DataViz

* [Flask](https://github.com/pallets/flask)

* [Dash](https://github.com/plotly/dash)

* [pandas-profiling](https://github.com/pandas-profiling/pandas-profiling)

* [missingno](https://github.com/ResidentMario/missingno)

[qm-contrib]: https://img.shields.io/static/v1.svg?label=&message=contributor&color=1A829E

[qm-contrib-head]: https://img.shields.io/static/v1.svg?label=QM&message=open-source&color=1A829E