Second Order Optimization - The Math of Intelligence #2 episode artwork

EPISODE · Jun 23, 2017 · 10 MIN

Second Order Optimization - The Math of Intelligence #2

from Siraj Raval

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 Please Subscribe! And like. And comment. That's what keeps me going. 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 Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ And please support me on Patreon: https://www.patreon.com/user?u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/

Episode metadata supplied by the publisher feed · Published Jun 23, 2017

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 Please Subscribe! And like. And comment. That's what keeps me going. 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 Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ And please support me on Patreon: https://www.patreon.com/user?u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/

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Second Order Optimization - The Math of Intelligence #2

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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...

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