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Episode 19: Is Julia Better Than JAX For Machine Learning?

David and Randy respond to an article that makes the case for JAX over Julia for machine learning, particularly when applied to solving differential equations.

David also shares a series of workshops hosted by the Julia Gender Inclusive community, as well as a new package by Elias Carvalho for creating truth tables from Julia expressions, and Randy explores a YouTube series and set of Pluto notebooks all about partially observable Markov processes.

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