awesome/machine-learning
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Merge pull request #535 from xhlulu/patch-1

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Add Dash - Interactive, Reactive Web Apps for Python
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Joseph Misiti 2018-09-27 23:31:50 -04:00 committed by GitHub
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@ -1036,11 +1036,13 @@ be
* [Bowtie](https://github.com/jwkvam/bowtie) - A dashboard library for interactive visualizations using flask socketio and react.
* [lime](https://github.com/marcotcr/lime) - Lime is about explaining what machine learning classifiers (or models) are doing. It is able to explain any black box classifier, with two or more classes.
* [PyCM](https://github.com/sepandhaghighi/pycm) - PyCM is a multi-class confusion matrix library written in Python that supports both input data vectors and direct matrix, and a proper tool for post-classification model evaluation that supports most classes and overall statistics parameters
* [Dash](https://github.com/plotly/dash) - A framework for creating analytical web applications built on top of Plotly.js, React, and Flask
<a name="python-misc"></a>
#### Misc Scripts / iPython Notebooks / Codebases
* [Map/Reduce implementations of common ML algorithms](https://github.com/Yannael/BigDataAnalytics_INFOH515): Jupyter notebooks that cover how to implement from scratch different ML algorithms (ordinary least squares, gradient descent, k-means, alternating least squares), using Python NumPy, and how to then make these implementations scalable using Map/Reduce and Spark.
* [BioPy](https://github.com/jaredthecoder/BioPy) - Biologically-Inspired and Machine Learning Algorithms in Python.
* [SVM Explorer](https://github.com/plotly/dash-svm) - Interactive SVM Explorer, using Dash and scikit-learn
* [pattern_classification](https://github.com/rasbt/pattern_classification)
* [thinking stats 2](https://github.com/Wavelets/ThinkStats2)
* [hyperopt](https://github.com/hyperopt/hyperopt-sklearn)