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I got curious about KL Divergence after reading the Variational Auto Encoder Paper. So, I decided to investigate it to get a better intuition. KL Divergence is a measure of how one probability distribution $P$ is different from a second probability distribution $Q$. If two distributions are identical, their KL div. should be 0. Hence, by minimizing KL div., we can find paramters of the second distribution $Q$ that approximate $P$. In this post i try to approximate the distribution $P$ which is ...