updated readme

readme.md

@@ -1,13 +1,17 @@
-# Machine learning for material recognition with a TENG
-(Bi)LSTM for name classification.  
-More information on the project are [on my website](https://quintern.xyz/en/teng.html).
+# Machine learning for material recognition with a triboelectric nanogenerator (TENG)
+This project was written for my bachelor's thesis.
+
+It was written to classify TENG voltage output from pressing it against different materials.
+Contents:
+- Data preparation/plotting/loading utilites
+- (Bi)LSTM + fully connected + softmax model for name classifiying TENG output
+- Progress tracking utilities to easily find the best parameters
 
 ## Model training
-Adjust the parameters in `main.py` and run it.  
+Adjust the parameters in `main.py` and run it.
 All models and the settings they were trained with are automatically serialized with pickle and stored in a subfolder
 of the `<model_dir>` that was set in `main.py`.
 
 
 ## Model evaluation
 Run `find_best_model.py <model_dir>` with the `<model_dir>` specified in `main.py` during training.
-