Classificar imagens de nuvens no Cloud com o AutoML Vision avaliações

41916 avaliações

Anderson M. · Revisado há over 3 years

Dipsikha T. · Revisado há over 3 years

Chandra K. · Revisado há over 3 years

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Joe W. · Revisado há over 3 years

Aashis K. · Revisado há over 3 years

great understandable presentation

chollangi c. · Revisado há over 3 years

Riccardo S. · Revisado há over 3 years

sangavi k. · Revisado há over 3 years

Yi L. · Revisado há over 3 years

Cheng L. · Revisado há over 3 years

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賢司 塩. · Revisado há over 3 years

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Aditi S. · Revisado há over 3 years

Rahul J. · Revisado há over 3 years

Definitely the worst lab so far. The instructions are out of date - e.g. CSV-based image import seems to require a header row, absent in the original data.csv; it succeeded only after I've added the labels; visiting web UI of GCS is confusing as it serves no purpose - CLI is used to copy all the files *and to list them*, so visit to UI is redundant and makes the student wonder if they've missed someting. I feel like I've learned nothing, it was just copying files from one bucket to another and clicking some buttons with default settings... Now for the constructive part: how about providing a pre-trained model to copy to student's account instead of telling them to "see the screenshots"? How about changing the dataset used for training (image sizes range from 50 KB to whopping 9 MB!) - if you're just showcasing the tool, maybe something smaller/simpler would suffice? I'm no expert in ML, but I suppose that teaching the model to recognize items on 64x64 px icons would be much faster. Or maybe provide 2 or 3 pre-trained models with different quality of input images to show the differences? Please review the lab and update the instructions, in its current form it's a waste of time and resources.

Andrzej G. · Revisado há over 3 years

Vinayak H. · Revisado há over 3 years

José T. · Revisado há over 3 years

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