Update chapters/en/unit5/generative-models/gans-vaes/stylegan.mdx

Co-authored-by: Merve Noyan <[email protected]>

chapters/en/unit5/generative-models/gans-vaes/stylegan.mdx

@@ -6,7 +6,7 @@ What you will learn in this chapter:
 - StyleGAN1 components and benefits
 - Drawback of StyleGAN1 and the need for StyleGAN2.
 - Drawback of StyleGAN2 and the need for StyleGAN3.
-- Usecases of StyleGAN.
+- Use cases of StyleGAN
 
 ## What is missing in Vanilla GAN
 Generative Adversarial Networks(GANs) are a class of generative models that produce realistic images. But it is very evident that you don't have any control over how the images are generated. In Vanilla GANs, you have two networks (i) A Generator, and (ii) A Discriminator. A Discriminator takes an image as input and returns whether it is a real image or a synthetically generated image by the generator. A Generator takes in noise vector (generally sampled from a multivariate Gaussian) and tries to produce images that look similar but not exactly the same as the ones available in the training samples, initially, it will be a junk image but in a long run the aim of the Generator is to fool the Discriminator into believing that the images generated by the generator are real.