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[P] pyTsetlinMachineParallel released – parallel interpretable machine learning with propositional logic.

[P] pyTsetlinMachineParallel released - parallel interpretable machine learning with propositional logic.

Back to bits

Have worked on speeding up the Tsetlin Machine. It turns out that merely synching at the clause level maintains robust inference, with little loss in parallelism. E.g., 25 threads reach the current Fashion MNIST test accuracy peak of 91.49% 18 times faster than a single thread.

pyTsetlinMachineParallel provides a multi-threaded implementation of the Tsetlin Machine, Convolutional Tsetlin Machine, Regression Tsetlin Machine, and Weighted Tsetlin Machine, with support for continuous features and multi-granular clauses.

https://github.com/cair/pyTsetlinMachineParallel

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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.