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As someone who took calculus, linear algebra and a couple CS related courses on my economics program in university 6 years ago, I wish I had someone around who

by whatch 8y ago

As someone who took calculus, linear algebra and a couple CS related courses on my economics program in university 6 years ago, I wish I had someone around who could explain me (better) why and how all those courses can be helpful to me. ML and AI were not big those days (at least I didn't hear much about them as I do know). Now, looking for my first python web developer job and dreaming about switching to something more ML related eventually, I am thankful I hadn't completely fail those courses, but feel extremely sorry I didn't study them properly.

deleted 8y ago
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gaius 8y ago
You can get a fair way these days without knowing any maths at all, the tools and libraries are very good and there are a ton of tutorials and sample code out there. But what a grasp of the maths really gives you is intuition which you will really, really need as soon as you go off the beaten track. Without it you will just get bogged down in hyperparameter sweeps and other tasks that just don’t scale
rrmm 8y ago
Calculus provides the foundation for finding the optimal solutions to the math problems you set up. This is through a few methods: differential curvature for gradient descent, and the theory of real functions provides gaurantees regarding convex functions and more generally, where optimal values can occur (exterma and stationary points). Linear algebra is in general the language used to set up and solve the problems systematically (on a computer). Probability and stats build on calculus to provide formal methods to formulate the problems. ML adds techniques to this area to tackle problems focused around getting machines to learn and solve specific domain problems. Deeper math is probably more necessary when developing machine learning algorithms. To apply machine learning techniques, less math is required. But knowing Lin algebra and matrix manipulations (Matlab, R fluency, etc) will not be wasted effort either way.

by whatch 8y ago

deleted 8y ago

[deleted]

gaius 8y ago

You can get a fair way these days without knowing any maths at all, the tools and libraries are very good and there are a ton of tutorials and sample code out there. But what a grasp of the maths really gives you is intuition which you will really, really need as soon as you go off the beaten track. Without it you will just get bogged down in hyperparameter sweeps and other tasks that just don’t scale

rrmm 8y ago

Calculus provides the foundation for finding the optimal solutions to the math problems you set up. This is through a few methods: differential curvature for gradient descent, and the theory of real functions provides gaurantees regarding convex functions and more generally, where optimal values can occur (exterma and stationary points). Linear algebra is in general the language used to set up and solve the problems systematically (on a computer). Probability and stats build on calculus to provide formal methods to formulate the problems. ML adds techniques to this area to tackle problems focused around getting machines to learn and solve specific domain problems. Deeper math is probably more necessary when developing machine learning algorithms. To apply machine learning techniques, less math is required. But knowing Lin algebra and matrix manipulations (Matlab, R fluency, etc) will not be wasted effort either way.