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so here you see a classifier that takes a look at this image and assigns one of many many labels actually one of a hundred and one labels as you can see here and one of the labels is a photo of guacamole a type of food and it assigns a really high probability to that as opposed to like the the second prediction which is ceviche um so you know classifier pretty good okay uh take a look at this classifier out of 397 labels it correctly identifies that this is a television studio um you can go on right here and so this is a photo of an airplane whenever theres a green bar at the top it means that the respective classifier has this correctly whenever there is an orange bar its an incorrect label with the the green bar being the correct label so you can see here these classifiers perform sometimes pretty well on these examples and sometimes not but what you can distinctly see is that these are all from different data sets so different tasks there is a satellite image there is a car and yo
