▲ 71 ▼ I was right, it is (fedia.io) submitted 1 year ago by Lukstru@fedia.io to c/science_memes@mander.xyz 7 comments fedilink hide all child comments Watched Primer's new video (https://www.youtube.com/watch?v=GkiITbgu0V0 watch it!) and stumbled over this gem
[–] abbadon420@sh.itjust.works 10 points 1 year ago (3 children) Took me 17 minutes to get the joke. Good video though. permalink fedilink source hideshow 3 child comments replies: [–] UnRelatedBurner@sh.itjust.works 3 points 1 year ago (2 children) can I get my instant gratification please? permalink fedilink source parent hideshow 2 child comments replies: [–] abbadon420@sh.itjust.works 6 points 1 year ago* (1 child) Sure, here's a TLDW. Imagine you're training an AI model. You feed it data and test if it comes up with a good answer. Of course it doesn't do that right away, thats why you.re training it. You have to correct it. If you correct the model by correcting the errors, you get overcompensation problems. If you correct it on the differences between the errors, you get a much better correction. The term for that is LOSS. You correct on LOSS in stead of on pure ERROR. permalink fedilink source parent hideshow 1 child comment replies: [–] UnRelatedBurner@sh.itjust.works 2 points 1 year ago haha, loss permalink fedilink source parent
[–] UnRelatedBurner@sh.itjust.works 3 points 1 year ago (2 children) can I get my instant gratification please? permalink fedilink source parent hideshow 2 child comments replies: [–] abbadon420@sh.itjust.works 6 points 1 year ago* (1 child) Sure, here's a TLDW. Imagine you're training an AI model. You feed it data and test if it comes up with a good answer. Of course it doesn't do that right away, thats why you.re training it. You have to correct it. If you correct the model by correcting the errors, you get overcompensation problems. If you correct it on the differences between the errors, you get a much better correction. The term for that is LOSS. You correct on LOSS in stead of on pure ERROR. permalink fedilink source parent hideshow 1 child comment replies: [–] UnRelatedBurner@sh.itjust.works 2 points 1 year ago haha, loss permalink fedilink source parent
[–] abbadon420@sh.itjust.works 6 points 1 year ago* (1 child) Sure, here's a TLDW. Imagine you're training an AI model. You feed it data and test if it comes up with a good answer. Of course it doesn't do that right away, thats why you.re training it. You have to correct it. If you correct the model by correcting the errors, you get overcompensation problems. If you correct it on the differences between the errors, you get a much better correction. The term for that is LOSS. You correct on LOSS in stead of on pure ERROR. permalink fedilink source parent hideshow 1 child comment replies: [–] UnRelatedBurner@sh.itjust.works 2 points 1 year ago haha, loss permalink fedilink source parent
[–] sniggleboots@europe.pub 6 points 1 year ago I watched that video mere hours ago! permalink fedilink source
[–] Septimaeus@infosec.pub 5 points 1 year ago* (last edited 1 year ago) Gradient descent? It’s loss. permalink fedilink source
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