Hi Sir, you have done such great explanation in this video. Your series video on Gaussian Process Regression very helpful. However, I have a curiosity on how to decide to use the best kernel for the any data? and how can I tune the value from kernel parameter like k1, k2, k3 and k4 to get the best fit for any dataset?
Sorry, I have a question. If our dataset will be multidimensional, how can we deal with this issue? you can imagine we have the dataset with dimensions x_train:(200,1280), y_train:(200,1280), x_test:(24,1280) and y_est:(24,1280).
@@learndataa thanks for your response 👍 Actually, 1280 can't be reduced. I should define a loss function and compare the real y and the prediction. I faced some problems when I wanted to import my dataset. My actual dataset has below dimension: X_train : 240, 64, 10, 2 Y_ train: 240, 1280 X_test: 24, 64, 10, 2 Y_test: 24, 1280 I changed my dimensions at first to the ones which I mentioned in the previous comment. But, I shouldn't reduce them. If it is possible guide me, please 🙏 And let me ask you about convolutional kernel; why didn't you define convolutional kernel?
Thank you for the detailed explanation with real-world data.
Hello sir, Thanks for your useful video.
One question, how would you implement a heteroscedastic gaussian to help determine noise variance?
Hi Sir, you have done such great explanation in this video. Your series video on Gaussian Process Regression very helpful.
However, I have a curiosity on how to decide to use the best kernel for the any data?
and
how can I tune the value from kernel parameter like k1, k2, k3 and k4 to get the best fit for any dataset?
@@learndataa Thank you Sir.
Sorry, I have a question. If our dataset will be multidimensional, how can we deal with this issue? you can imagine we have the dataset with dimensions x_train:(200,1280), y_train:(200,1280), x_test:(24,1280) and y_est:(24,1280).
@@learndataa thanks for your response 👍
Actually, 1280 can't be reduced. I should define a loss function and compare the real y and the prediction.
I faced some problems when I wanted to import my dataset. My actual dataset has below dimension:
X_train : 240, 64, 10, 2
Y_ train: 240, 1280
X_test: 24, 64, 10, 2
Y_test: 24, 1280
I changed my dimensions at first to the ones which I mentioned in the previous comment. But, I shouldn't reduce them.
If it is possible guide me, please 🙏
And let me ask you about convolutional kernel; why didn't you define convolutional kernel?
The same question bro,
did you find solution to your problem ?