"Just hide everything from the user" is a terrible design strategy. You have to figure out what to expose, and how, so that the user feels like the tool is a simple extension of the way they think about their problem.
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A good API is approachable and easy to use, while providing great expressivity and flexibility. You can only achieve this by designing your API to match the mental models of domain experts. An "algorithm in a black box" kind of API is easy to use, but provides no flexibility.
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Don't confuse packaging and design. Pick your tools wisely
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I'm on the Fringe between sharing my great AR ideas or sitting on them to capitalize. The Tesla/Edison conundrum
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Any good references you recommend on API design and best practices?
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I'm assuming this is in reference to
#fastai v1.0 (http://www.fast.ai/2018/10/02/fastai-ai/ …). I'd love to see you go into more detail about the differences between#Keras and this new framework. A lot a DL practitioners are probably scratching their heads about which one to choose right now.Thanks. Twitter will use this to make your timeline better. UndoUndo
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fastai is good for some things, not all. Consider it a “gateway” API. Since it’s built on pytorch, which does give much more flexibility, I don’t see the downside
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Ooh, lordy, this is what we're up against with quantum algorithms. We still have perhaps hundreds of people who understand any given algorithm well, and in a decade (perhaps two) we'll have tens or hundreds of thousands using them. Can they be black boxes?
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