Coding gaussian process regressors FROM SCRATCH in python

[@reallyanotheruser7290]

Out of what feels like two dozen tutorials and explanations i found this is actually what made me understand it

[@OliverJanShD]

This is amazing! Thank you for providing this approach, it really helped me understand GPR a lot better

[@youngzproduction7498]

This is a solid gold for me. I like learning anything in a visual way which I can interact with it. Thanks for your effort.

[@TaylorSparks]

underrated video! Thanks for making this great content. This helped me quite a bit as I prepared a lecture on this topic for my materials science students.

[@nicolaipalm7563]

Thanks! Glad I could help you 🤓

[@ross302ci]

Extremely helpful for understanding GPRs, thank you!

[@muhammadrayyan4389]

You are amazing!!!....thanks for helping me in studying for my Green Light meeting which is due in less than 2 days!!!!..this video gave me a great confidence!!!!...once again thank you very much!!!!!

[@satadrudas3675]

This is so underrated. Good job anyway

[@33gbm]

Excellent material you provided here; I just came back to the video to congratulate your efforts on the content hahaha Thank you, man!

[@umutkorkut8555]

Very cool and easily digestible content, loved it!

[@pouyaaghaeipour8336]

Can we have access to the notebook file?

[@amothe83]

Excellent , the best video on gaussian process regressors

[@eva__4380]

This was really helpfull for me in understanding GP thankyou so much for your efforts

[@ebrahimfeghhi1777]

Great video, thank you!

[@brendarang7052]

Nice one! Thank you.

[@swisscheese9590]

Great video. Would you do a follow-up on hyperparameter optimization using marginal log-likelihood in the loss function? Also, a visualization example using multi-input GPs would be interesting as well. Or multi-output GPs.

[@azd.zayoud]

Well done! # Writing comments would be helpful for beginners if it is put in a context of solving a problem/examples : it will be more useful. Thanks!

[@NamNguyen.ee2]

Thank you so much!

[@erniwidyawati6254]

Can you suggest how to do GPR with poisson likelihood? Should i use approximation for inference like using laplace approximation?

[@komuna5984]

Absolutely Mindblowing Work! Keep it up. May Allah bless you. 🙂

[@amalroymurali3457]

Excellent stuff. Thanks!

[@icoop99]

Very nice video - thank you very much :D

[@kaushikgupta1410]

thanks for this amazing explanation

[@farhadazadi]

This was fantastic

[@jaimesastre6393]

Amazing 👌🙏👌 Access to the notebook would be great 🙏🙏🙏

[@nicolaipalm7563]

thanks! 😀 link to the notebook is in the description

[@IvanStar96]

Is sigma 0 or 1 in this example? The title of the graph says it is 0, but doesn't the code say it equals 1?

[@nicolaipalm7563]

Yes that is correct. 😀

[@fatismajli2490]

Nice Video!

[@MrSchwede91]

Kommt die Fortsetzung noch? Bisher alles sehr gut beschrieben...

[@junaidlatif2881]

Can we use your method on our data?

[@paretos-com]

sure! What kind of data is it?

[@yeshuip]

hello, how to feed sequence of input data to train sequences of outputs

[@nicolaipalm7563]

With this framework you can feed multidimensional input to the GPR. In order to obtain multi dimensional output you simply train a GPR for each component of the output vector. :)

[@yeshuip]

@@nicolaipalm7563 thanks for the reply. but my question was is it possible to feed N*d matrix as input and N*2 aa output. where N represents the input sequence and d represents dimension of features and N as output sequence number and 2 as number of output features