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Comment by gumballhead | Hacker News Reader

Thank you for creating these courses and putting them online! I started part 1 a few weeks ago. I couldn't agree more with your teaching style. I'm someone who started college as a computer science major and couldn't finish, because it felt like I was pounding my head on the wall trying to learn abstract math when what I really wanted to do was build stuff.

I finished my degree with economics, went to work in finance, and worked my way to being a developer by first doing advanced Excel, then Access, then database servers, then web development, then native development, and now more math heavy research and development. As the need arises, I keep going further down the stack and gaining more understanding. It's much more natural (for me) to learn that way.

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> because it felt like I was pounding my head on the wall trying to learn abstract math when what I really wanted to do was build stuff.

But you do need to know abstract math to build stuff, particularly if what you want to build is deep learning stuff, both models and implementations.

For example, how do you expect to understand how to minimize an utility function if you have no idea of what a gradient is, how you calculate it, and why you want to descend through it.

> For example, how do you expect to understand how to minimize an utility function if you have no idea of what a gradient is, how you calculate it, and why you want to descend through it.

The course teaches all those things - as the comment you're replying to states, you go deeper and deeper during the course to understand all the details.

There's been a lot of research into teaching strategies that shows that this is often a more effective approach for many people than the bottom up approach widely used in math and CS. It doesn't mean that you learn any less of the foundations - just that it's in a different order.

> There's been a lot of research into teaching strategies that shows that this is often a more effective approach for many people than the bottom up approach widely used in math and CS.

I seriously doubt that anyone can effectively learn linear algebra, multivariate calculus, optimization and regression models from an onlone tutorial on deep learning. These are subjects whose basics alone take multiple semester-long courses. If a bottom-down approach was remotely effective, no one would bother teaching the basics.

Do you think it's necessary to have a rigorous understanding of all of those topics before creating a machine learning model? And that you don't learn from interacting with it, even if you don't fully understand how it works? For machine learning in particular I think that's pretty ironic.

> If a bottom-down approach was remotely effective, no one would bother teaching the basics.

This is a purist approach.

Sometimes, you sacrifice the details to broaden the audience. This has the result of getting more people interested.

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