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Gradient Methods for Machine Learning
Published on 2007-02-259994 Views
Gradient methods locally optimize an unknown differentiable function, and thus provide the engines that drive much machine learning. Here we'll take a look under the hood, beginning with brief overvie
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Presentation
Course Overview00:01
Classical Gradient Methods01:34
Function Optimization04:01
Methods by Gradient Order06:46
Direct (Gradient-Free) Methods08:22
Prototypical Direct Method09:25
Direct Methods: Advantages13:23
Direct Methods: Disadvantages18:33
Gradient Descent21:42
Gradient Descent: Disadvantages25:08
Newton’s Method31:52
Gauss-Newton Approximation38:32
Levenberg-Marquardt48:09
Quasi-Newton: BFGS50:42
Conjugate Gradient53:15
Conjugate Gradient: Properties57:36