From 1c97683d54afa2f32c6e4f79eec26716baa2aa24 Mon Sep 17 00:00:00 2001
From: Nando Farchmin <nando.farchmin@gmail.com>
Date: Fri, 1 Jul 2022 19:23:47 +0200
Subject: [PATCH] Fix typo

---
 doc/basics.md | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/doc/basics.md b/doc/basics.md
index 80b57d3..1b7bdbe 100644
--- a/doc/basics.md
+++ b/doc/basics.md
@@ -129,11 +129,11 @@ The empirical regression problem then reads
 > A _loss functions_ is any function, which measures how good a neural network approximates the target values.
 
 Typical loss functions for regression and classification tasks are
-- mean-square error (MSE, standard $`L^2`$-error)
-- weighted $`L^p`$- or $`H^k`$-norms (solutions of PDEs)
-- cross-entropy (difference between distributions)
-- Kullback-Leibler divergence, Hellinger distance, Wasserstein metrics
-- Hinge loss (SVM)
+  - mean-square error (MSE, standard $`L^2`$-error)
+  - weighted $`L^p`$- or $`H^k`$-norms (solutions of PDEs)
+  - cross-entropy (difference between distributions)
+  - Kullback-Leibler divergence, Hellinger distance, Wasserstein metrics
+  - Hinge loss (SVM)
 
 To find a minimizer of our loss function $`\mathcal{L}_N`$, we want to use the first-order optimality criterion
 
-- 
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