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Greatest a r t ist s i n t he world. I’m not su re t hat I cou ld d raw you a decent cat. So, you k now, this is this is not confined to just Computing. I s it t h i s? Ye a h , t h a t’s t r u e . T h a t’s rea l ly t r ue. But i f you t a ke L et’s do l i ke a rea l ly simple example of a generative model say you you give your network one thing it looks like this and then you give it another one you like these your training samples looks like this is another one that looks like this and then what are those dogs? A re your systems instances of something on two Dimensions? Ye a h . I m e a n r i g h t n ow it’s l it e r a l l y j u s t d a t a w e just it doesn’t matter what it is. Just some yeah, these are pieces of data points. And so t hese a re t he t hings you’re g iv ing it a nd t h e n it w i l l l e a r n you c a n t r a i n it . You w i l l l e a r n a model and the model it might learn is somet h i n g l i ke t h is r ight? It’s f ig u red out t ha t t hese dot s a l l l ie a lon g a pa t h a nd i f it’s model was always to draw a line, then it could learn by adjusting the parameters of that line. It would move the line around until it found a line. That was a good fit and generally gave you a good prediction. But then if you were to ask you this model. Now make me a new one. Unless you did something clever what you get is probably this because that is on average the closest to any of these because any of these dots you don’t k now if t hey’re going to be above or below or do you know to the left or the right? T here’s no pa t ter n t here. It’s k i nd of random. So the best place you can go that will minimize your error is to go right on the line every time but anybody looking at this will say wel l t ha t’s fa ke. T ha t’s not a plausible example of something from this distribution, even though for a lot of the like error functions that people use

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