Good explanation. It is heartwarming to hear a South African accent on a youtube video every now and then. My ML model predicts with a 90% probability that this speaker is from Pretoria.
Hello, this was a great tutorial and I really appreciate the help. I am wondering what np.random.seed(1) is used for within this code? Any explanation would be great! Thank you!
Hello i am a phd student. Recently I am learing the gaussian process regression and I also follows your discussion. can we communicate each other as i need a help from you for some points
@@coryreedrobbins Hey Cory you can surely check out the Gaussian process lectures of Nando De Freitas ( ruclips.net/video/MfHKW5z-OOA/видео.html) they can help you get a really good idea. The playlist of the machine learning course is pretty good actually. I had taken the above-mentioned Udemy course for my own understanding it gives you a fairly good intuition too as a beginner-level course. It is a bit hard to understand GPR from research papers, do read the book of Rasmussen and Williams and can also refer to this particular blog of Martin Krasser (krasserm.github.io/2018/03/19/gaussian-processes/) and can check out some videos of GPSS, the summer school of Sheffield university is pretty good too.
Very helpful video. Thank you so much ❤
Good explanation. It is heartwarming to hear a South African accent on a youtube video every now and then. My ML model predicts with a 90% probability that this speaker is from Pretoria.
Great video!
Do you have a link to your used dataset (the .csv file)?
Could you please give an example of predicting your data set in the future? it would be very helpful to me, thank you ;)
Can you please share the dataset? It would be really helpful.
Hi, how do I get error prediction?
Quite helpful, thanks!
What is this to do 10,000 samples we need to write 10000 numerical values this code not very basic to use and understand for sample dataset
can we use this code for gaussian process classification?
Absolutely.
Hi can you share code file?
Hello, this was a great tutorial and I really appreciate the help. I am wondering what np.random.seed(1) is used for within this code? Any explanation would be great! Thank you!
you set a random.seed to get the same "random" values every time you run the code. Otherwise results won't be comparable.
Line 32: instead of writing down the whole list you can do: [float(x) for x in range(0, 178)] - much simpler
Hello i am a phd student. Recently I am learing the gaussian process regression and I also follows your discussion. can we communicate each other as i need a help from you for some points
Hello dibyendu i am phd student too and i have started working on GPR from last 6 months it will be great if we can get in touch for discussion .
@@swarnendusekharghosh9539 we can communicate through email if you want!
I'm trying to learn this stuff too - engineer here. Have you guys found any interesting resources?
@@coryreedrobbins Hey Cory you can surely check out the Gaussian process lectures of Nando De Freitas ( ruclips.net/video/MfHKW5z-OOA/видео.html) they can help you get a really good idea. The playlist of the machine learning course is pretty good actually. I had taken the above-mentioned Udemy course for my own understanding it gives you a fairly good intuition too as a beginner-level course. It is a bit hard to understand GPR from research papers, do read the book of Rasmussen and Williams and can also refer to this particular blog of Martin Krasser (krasserm.github.io/2018/03/19/gaussian-processes/) and can check out some videos of GPSS, the summer school of Sheffield university is pretty good too.
Hello