What is the Recipe for catching false positives via fleet learning? In other words, how do I provide feedback that my car was phantom braking?
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Learning the identity (make label=input) for image segmentation tasks is also a good sanity check for model capacity.
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Really love this quote: “Many times it is possible to feel the network “struggle” to fit your data if it wiggles too much in some way, revealing instabilities.”
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Thanks for mentioning our webinar! (credit to
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nice motivational/therapy blog on training NN "We are in pursuit of correctness and are very willing to give up time for staying sane."
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Most important quote of the post: "but my personal experience is that the state of the art approach to exploring a nice and wide space of models and hyperparameters is to use an intern :). Just kidding."
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"I'm just kidding. But seriously, I've got them"https://www.youtube.com/watch?v=h14nVtDy-Dg&t=30 …
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1) I took cs231n and remember u standing at the auditorium entrance to greet us :) Thanks for that! 2) Can we get your permission to translate this to Arabic? I work with a team of volunteers to translate great articles and this would be very useful for non english speakers!
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Very concrete desciption! It seems that currently we are still far from fully unsuppervised solutions.
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Would love to know if you can confirm Tesla still uses Nvidia for Cloud, even though transitioning to Tesla edge chips, Training more intensive than Inference in the car, peace
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