Bayesian Optimization - Math and Algorithm Explained

Learn the algorithmic behind Bayesian optimization, Surrogate Function calculations and Acquisition Function (Upper Confidence Bound). Visualize a scratch implementation on how the approximation works iteratively. Finally, understand how to use scikit-optimize package todo hyperparameter tuning using bayesian optimization.

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[saleemun]

by far the clearest explanation of bayesian optimization, great work, thanks man!

[sm-pzer]

Very well simplified explanation. Thank you

[Xavier-Ma]

Wonderful explaination! Thanks professor.

[syedtalhaabidalishah]

what a video!!! simple and straight forward

[youssefbakadir]

First comment on this video :D, and basicaly the 666 subscriber!
Thanks a lot for this content it was very helpful! plz continue

[nicolehuang]

Thanks for your sharing, u explained clearer than my professor

[YuekselG]

is there a mistake in 9:10 ? there is 1 f(x) too much i think. Has to be N(f(x_1), ... (x_n) l o, C*)) / N(f(x_1), ... (x_n) l o, C)). Can anyone confirm this? ty

[-kaito]

Thanks I think now I would be able to use it in hyperparameter training without having to check every single combination.

[masyitahabu]

It very good explaination but for the acquisition function I hope u can explain more detail how it help surrogate choose next point.

[hanserj]

Great explanation. Do you sample more than one point at each iteration (sampled and evaluated in the target function)? or are the 23 points that you have in iteration 17 cumulative? I am asking that because the "sampled points" in the plots increases at each iteration.

[ranaiit]

Thanks....missing negative sign in exponent of Gaussian function !

[mikehawk]

Why do you add the mean of the predicted points back to the predicted points?