Multivariate Calculus – Constrained optimisation

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Course 2 - Mathematics for Machine Learning Multivariate Calculus, Module 5 - optimisation

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Mathematics for Machine Learning: Multivariate Calculus https://www.youtube.com/playlist?list=PL2jykFOD1AWaL4_-bdidPfIWe765jOgfL

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About this course: This course offers a brief introduction to the multivariate calculus required to build many common machine learning techniques. We start at the very beginning with a refresher on the “rise over run” formulation of a slope, before converting this to the formal definition of the gradient of a function. We then start to build up a set of tools for making calculus easier and faster. Next, we learn how to calculate vectors that po...

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Jess Stuart 

7:52 The solutions aren't marked on the contour plot or 3-D graph.


Jess Stuart 

I would call g(x,y) the "radius squared" function. The gradient of the radius squared function points in the direction normal to the circle, because this gradient points in the direction the radius (or radius squared) increases the "fastest" with a small displacement in the neighborhood around any point on the circle.