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Gradient Methods for Machine Learning
Published on Feb 25, 20079969 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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Chapter list
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
Newton’s Method34:37
Gauss-Newton Approximation38:32
Levenberg-Marquardt48:09
Quasi-Newton: BFGS50:42
Conjugate Gradient53:15
Conjugate Gradient: Properties57:36