Machine Learning for Machine Learning

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About [ML]²

We are a not-for-profit organization that focuses on addressing machine learning limitations using machine learning. [ML]² is a space for machine learning enthusiasts to collaborate and find solutions for such limitations.

Interpretable Deep Learning. Yay!

The most practical problem with Deep Learning is the percieved lack of interpretability and explainability. We, at [ML]², are developing a framework to make every Deep Learning Model ineterpretable.


Make "interpretability" a first class method on a model object

Interoperable ML. Yay!

Can the producer and consumer of an ML model be separated? IMLY does it by providing a reference dnn implementation to a target specificaton.


Provide every ML algorithm with an equivalent DNN implementation

MLNet: The WordNet for ML!

ML is progressing at a brisk rate of 100 papers per month, modestly speaking. Curate that knowledge, and make it avaiable to both humans and algorithms.


Make ML machine-understandable.


Bangalore, India