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Abstract:

We are in a period of active development of new deep learning techniques, adding to the already mature area of traditional machine learning. This is leading to a vast and ever evolving field of implementations which can be disorienting.

In this webinar, I will guide you through a map of the current frameworks, organizing them based on their domain (machine learning vs deep learning) and the languages required to use them. I will also talk about the various automatic differentiation options available.

To narrow such a large topic, I am limiting the map to frameworks that can be used from Python, Julia, and R.