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T hen t ha t’s rea l ly g ood. S o you t r a i n t hem a re i n a c yc l e ef fe c t i v e l y. You you g i v e the discriminator a real image get its output. Then you generate a fake image and get the discriminator that And then you give it a real so t he It’s a lter na t i n g rea l i ma g e fa k ing is rea l ima ge fa ke ima ge. Usua l ly, I mea n there are things you can do where you train them at different rates and what happened by default. It doesn’t generate to get a n y h e l p w it h t h i s a t a l l , or i s it p u r e l y? Ye s . S o if you this is this is like part of what makes t h is especia l ly clever. Act ua l ly, t he generator does get help because If you set up the Net work ’s r ight , you ca n u se t he g r a d ient of t he d i s c r i m i n a t o r. To t r a i n t h e g e n e r a t o r S o w h e n? I know you done backpropagation before about how neural networks are trained its gradient descent. Right? And in fact, we t a l ked about t his in li ke 201 4 pure. If you’re a you’re a blind person climbing a mount a in or you r it’s rea l ly f u n ny a nd you’re cl i mbi n g a m ou nt a i n . You c a n on l y s e e d i r e c t l y w h a t’s u n d e r n e a t h you r ow n fe e t . You c a n s t i l l climb that mountain if you just follow t h e g r a d ie nt . You j u s t l o o k d i r e c t l y u n d e r m e , which way is t he you k now, which way is the ground sloping. This is what we did the hill c l i m b a l g or it h m . E x a c t l y. Ye a h , s om e t i m e s people call it hill climbing. Sometimes people ca l l it g r a d ient descent . It’s t he sa me met aphor upside dow n ef fectively if we’re climbing up or were climbing down your training them by gradient descent, which means that You’r e n ot j u s t you’r e n ot j u s t a b l e t o s a y. Ye s , t h a t’s g o o d . No, t h a t’s b a d . You’r e actually able to say and you should adjust your you should adjust your weights in this direction so that you’ll move down the gradient.

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