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Any resource/link you know of that further develops your point?
by BlueUmarell 1y ago
Any resource/link you know of that further develops your point?
- easygenes 1y agoCMU lecture notes [0] I think approach it in an intuitive way, starting from the Gaussian noise linear model, deriving log-likelihood, and presenting the analytic approach. Misses the bridge to gradient methods though. For gradients, Stanford CS229 [1] jumps right into it. [0] https://www.stat.cmu.edu/~cshalizi/mreg/15/lectures/06/lecture-06.pdf https://www.stat.cmu.edu/~cshalizi/mreg/15/lectures/06/lectu... [1] https://cs229.stanford.edu/lectures-spring2022/main_notes.pdf https://cs229.stanford.edu/lectures-spring2022/main_notes.pd...
- BlueUmarell 1y agoThanks! will have a look..