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really be this. And keep doing that in the system will eventually learn but if you were actually teaching a person to recognize the numbers if you were teaching a child, you wouldn’t do that. Like if you if you’d been teaching them for a whi le presenting t hem a nd you k now, get ting t he response in correcting them and so on and you noticed that they can do, you know with w it h 23 45689. T hey’re get ting li ke 70 80 percent, you know accuracy recognition rate, but one in s e v e n , i t ’s l i k e 5 0 /5 0 b e c a u s e they keep any time they get a 1 or a 7 they just g uess because they can’t tell the difference bet ween them. If you noticed that you wouldn’t keep training those other numbers, r i g h t? You w ou l d s t op a n d s a y w e l l , you k n ow what, we’re just going to focus on one a nd seven because t h is is a n issue for you. I’m going to keep showing you ones and sevens and correcting you until the error rate on ones and 7s comes up comes down to the error rate t h a t you’r e g e t t i n g on you r ot h e r nu m b e r s . You’r e focusing the training on the area where t he st udent is fa i l i n g a nd t here’s A ba la nce t here when you’re teaching. Hu ma ns, because if you keep relentlessly focusing on their weaknesses and ma k ing them do stuff they can’t do all the time. They will just become super discouraged and give up but neural net work s don’t have feel i n g s yet . S o t ha t’s rea l ly n ot a n i s s u e . You c a n j u s t c ont i nu a l l y h a m m e r on the weak points find whatever t hey’re hav ing t rouble w it h a nd focus on t hat a nd so that behavior and I think some people have had teachers where it feels like this. It feels li ke a n adversa r y, r ight? It feels li ke they want you to fail. So in fact, you can make them an actual adversary if you have some process, which is genuinely doing its best to make the network give us high and error as possible that will produce this effect where if

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