Backprop intuition — signal paths through deep systems

1:12:44what you do is you initialize the network with a bunch of random stuff. Usually it's like random weights. You can get more complicated that way. Um what it's going to do is it's going to have a guess at an answer. It's going to compare its guess to the real thing. Compute a loss, >> which is how different was my guess from the real thing. and then propagate that back through the network and change the weights such that the next time it's going to be a bit closer to the correct answer. That's what back propagation is. >> Literally, the movie Tenant is a movie about humans back propagating in like real life. It's like I just made that connection in my head right now, but

1:13:24that's literally what they were doing. It's funny. >> That's so funny [laughter] actually. Yeah. Um, so that that that's effectively what back propagation is. And and really what you're doing is like the chain rule in calculus where you take the change here and you multiply it. You take a derivative here and you multiply it to the next layer to the next layer to the next layer and that's how you calculate how much you want to change your weights by. Okay. It's a product of these derivatives, >> right? >> Okay. Um something called a Jacobian matrix. We don't have to we don't have to get into what this Jacobian matrix is. But here is the key thing. Okay. Effectively, whenever you're doing back propagation, the amount that the weights

1:14:05here change is based on a product of all of these numbers. >> Right?