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Architectural and Organizational Patterns in Machine Learning with Nishan Subedi - #462
The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
Machine Learning
The library is built on top of a cuberne's orchestration engine, where all the details internally are abstracted away from you. What it works on is the intervase of communities, where you provide an intervase to specify different just like value ranges. Once it knows how to fiddle around, how to figure out what's good, what's bad, it does that for you. And so by having these various stages, you can map the output of each stage into standard language and vocabulary as well.
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