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#Singular_value_decomposition #Matlab #machine_learning #Compression #Data_science #eigenfaces #Linear_algebra #Principal_component_analysis #SVD #PCAКомментарии:
Thank you so much for all the effort. I suppose the fact that you used first 64 columns of U (and not V) while previously you had V in your notes and in your last two examples is because you had different rows and columns in this example [I mean you had features of the same experiment in each row previously while you have features of each experiment in one column here]. Am I right? Sorry if it's a stupid question but it's kind of made my mind so busy. Thanks again Steve for all the effort.
ОтветитьThis awkward moment when profesor Brunton shows the average face and its your face :D
I love this SVD course!!
could you share training data set code
Ответитьthanks, you are a legend
ОтветитьSteve, thanks for sharing!
Can you please clarify the logic behind subtracting the average face? How the resulting eigen faces will be different?
Mindblown
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