50+ days of Machine Learning

 Hello :) Today is Day 47! A quick summary of today: Looking at GANs from DeepLearning.AI's perspective Trying to find more KAIST courses 1) GANs with DeepLearning.AI GANs... a war between a generator and a discriminator Given X, the disc tries to discriminate between dogs and cats, and the generator, given random noise and a class, it tries to create the X and trick the discriminator How do we train GANs? We start with the discriminator We give it real and fake examples. It gives its prediction on the fake images, calculate the loss on the fakes, then give it the real images, and calculate the loss on the real images. And the disc loss in this case is the avg of both.  Next, Then we train the generator,  we make random noise again, make fake images, get the discriminator to evaluate the fake images, and we get the generator loss.  Also the reason we use random noise is so that we dont get the same image each time.  Next - problems with traditional GANs Mode collapse - that is when