Starting diffphys chapter

overview.md

@@ -144,9 +144,20 @@ Very brief intro, basic equations... approximate $f^*(x)=y$ with NN $f(x;\theta)
 
 learn via GD, $\partial f / \partial \theta$ 
 
+general goal, minimize E for e(x,y) ... cf. eq. 8.1 from DLbook
+
+$$
+  test \~ \approx eq \ \RR
+$$
+
+introduce scalar loss, always(!) scalar...
+
 Read chapters 6 to 9 of the [Deep Learning book](https://www.deeplearningbook.org),
 especially about [MLPs]https://www.deeplearningbook.org/contents/mlp.html and 
 "Conv-Nets", i.e. [CNNs](https://www.deeplearningbook.org/contents/convnets.html).
 
 **Note:** Classic distinction between _classification_ and _regression_ problems not so important here,
 we only deal with _regression_ problems in the following.
+
+maximum likelihood estimation
+