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diff --git a/posts/2019-03-13-a-tail-of-two-densities.md b/posts/2019-03-13-a-tail-of-two-densities.md
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 This is Part 1 of a two-part post where I give an introduction to
 differential privacy, which is a study of tail bounds of the divergence between
 probability measures, with the end goal of applying it to stochastic
-gradient descent.
+gradient descent. 
 
 I start with the definition of $\epsilon$-differential privacy
 (corresponding to max divergence), followed by