Update readme and metainfo, removed github and authors

README.md

-In this tutorial we will use the ElemNet neural network architecture (https://github.com/NU-CUCIS/ElemNet) to predict the volume per atom of inorganic compounds, where the open quantum materials database (OQMD) is used as a resource (specifically, the data is taken from Ward et. al., npj Comput. Mater. 2, 16028 (2016)).
+In this tutorial, we discuss how multilayer perceptrons, a standard neural-network architecture, can be employed for regression tasks. Specifically, we will use the ElemNet neural-network architecture to predict the volume per atom of inorganic compounds, where the Open Quantum Materials Database (OQMD) is used as a resource.

metainfo.json

@@ -6,7 +6,7 @@
   ],
   "email": "leitherer@fhi-berlin.mpg.de",
   "title": "Introduction to multilayer perceptrons (deep neural networks)",
-  "description": "In this tutorial, we will use the ElemNet neural network architecture (https://github.com/NU-CUCIS/ElemNet) to predict the volume per atom of inorganic compounds, where the open quantum materials database (OQMD) is used as a resource (specifically, the data is taken from Ward et. al., npj Comput. Mater. 2, 16028 (2016)).",
+  "description": "In this tutorial, we discuss how multilayer perceptrons, a standard neural-network architecture, can be employed for regression tasks. Specifically, we will use the ElemNet neural-network architecture to predict the volume per atom of inorganic compounds, where the Open Quantum Materials Database (OQMD) is used as a resource.",
   "notebook_name": "nn_regression.ipynb",
   "url": "https://gitlab.mpcdf.mpg.de/nomad-lab/analytics-tutorial-template",
   "link": "https://analytics-toolkit.nomad-coe.eu/hub/user-redirect/notebooks/tutorials/nn_regression.ipynb",