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 * [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)