Fix typo

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