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There's no reason to think using machine learning will always require lots of math and theory. In it's infancy (ie, recent popularity) as it is today perhaps, b
by jameslk 8y ago
There's no reason to think using machine learning will always require lots of math and theory. In it's infancy (ie, recent popularity) as it is today perhaps, but eventually I expect the field to have tooling that abstracts away complexity that is unapproachable to the masses. For example, there's plenty of complicated computer science algorithms, data structures and design patterns that have been abstracted into libraries and applications. Databases and browsers are good examples of that. Frameworks such as Unity for game development and Rails for web development as well.
When you really need to build something customized, knowing how things work under the hood will be required, but most cases will probably be addressed with pre-existing solutions that you configure and glue together.
- hyperpallium 8y agoI think Computational Fluid Dynamics is a good analogy. Final year engineering courses now just explain the maths, and then teach how to use commercial software packages. That's for if you're working to engineering standards. But there are also animation tools using CFD, that don't require maths at all. Just fiddle with it to get the effect you want. NB. It took CFD a long time to get to this point. It predates digital computers, and has an even older overall theory (though not fully understood). We don't have that for DL.