Gradient Descent and its variants are very useful, but there exists an entire other class of optimization techniques that aren't as widely understood. We'll learn about second order method variants, how they compare to first order methods, and implement our own in Python.

Code for this video (with challenge): https://github.com/llSourcell/Second_Order_Optimization_Newtons_Method

Alberto's Winning Code: https://github.com/alberduris

Ivan's Runner up Code: https://github.com/PiaFraus

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Course Syllabus: https://github.com/llSourcell/The_Math_of_Intelligence

More learning resources: https://web.stanford.edu/class/msande311/lecture13.pdf https://www.cs.toronto.edu/~hinton/csc2515/notes/lec6tutorial.pdf https://www.quora.com/In-mathematical-optimization-problems-the-first-derivative-is-often-used-Why-not-the-second-or-higher-order-derivatives https://en.wikipedia.org/wiki/Newton%27s_method_in_optimization https://www.youtube.com/watch?v=28BMpgxn_Ec&t=444s https://www.youtube.com/watch?v=42zJ5xrdOqo&t=438s

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