From ac8979f6770d2c1ed79b680fc720cbefd8d588c7 Mon Sep 17 00:00:00 2001
From: Yuchen Pei <me@ypei.me>
Date: Tue, 19 Mar 2019 16:20:14 +0100
Subject: minor

---
 posts/2019-03-13-a-tail-of-two-densities.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/posts/2019-03-13-a-tail-of-two-densities.md b/posts/2019-03-13-a-tail-of-two-densities.md
index 460364a..f3e409c 100644
--- a/posts/2019-03-13-a-tail-of-two-densities.md
+++ b/posts/2019-03-13-a-tail-of-two-densities.md
@@ -8,7 +8,7 @@ comments: true
 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
-- 
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