Update README.md

README.md

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 - GAN
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+## Generative Adversarial Network
+
+This repo contains the model and the notebook to this [this Keras example on WGAN](https://keras.io/examples/generative/wgan_gp/).
+Full credits to: [A_K_Nain](https://twitter.com/A_K_Nain)
+
 ## Wasserstein GAN (WGAN) with Gradient Penalty (GP)
 
 The original Wasserstein GAN leverages the Wasserstein distance to produce a value function that has better theoretical properties than the value function used in the original GAN paper. WGAN requires that the discriminator (aka the critic) lie within the space of 1-Lipschitz functions. The authors proposed the idea of weight clipping to achieve this constraint. Though weight clipping works, it can be a problematic way to enforce 1-Lipschitz constraint and can cause undesirable behavior, e.g. a very deep WGAN discriminator (critic) often fails to converge.