diff --git a/doc/basics.md b/doc/basics.md index 1b7bdbe6825c07f13b184efc79b9f8694c048fd2..a9ec093b08407a12086ee86063bbd1defa1ba6ff 100644 --- a/doc/basics.md +++ b/doc/basics.md @@ -128,12 +128,6 @@ The empirical regression problem then reads > **Definition** (loss function): > 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) To find a minimizer of our loss function $`\mathcal{L}_N`$, we want to use the first-order optimality criterion