diff --git a/README.md b/README.md
index 28a8fce94aec62983d79b6d100b9da4536b0e823..4eecc49c65bc51685fa39a0a4e84d4d9e2cb6565 100644
--- a/README.md
+++ b/README.md
@@ -50,10 +50,37 @@ python -m ipykernel install --user --name=virtualenv
 
 No you run the **notebooks** with the `virtualenv` kernel.
 
+Important note: double check if you installed torch+cu118 using:
+```bash
+pip freeze
+```
+Sometimes torch instead of torch+cu118 is installed. If so do the following:
+
+```bash
+pip uninstall torch
+```
 
+```bash
+pip install torch==2.0.1 torchvision==0.15.2 torchaudio==2.0.2 --index-url https://download.pytorch.org/whl/cu118
+```
+or check the following website to find the correct commands for using a different OS or conda instead of pip: [Pytorch - Previous Versions](https://pytorch.org/get-started/previous-versions/)
 ### Content
 
-...
+You can use this repository to reproduce our BWDF forecasting resulst based on pre-trained models. Set up an environment as described above and run the following notebooks to reproduce weeks W1 - W4:  
++ DMA_reproduce_w1_predictions.ipynb
++ DMA_reproduce_w2_predictions.ipynb: 
++ DMA_reproduce_w3_predictions.ipynb
++ DMA_reproduce_w4_predictions.ipynb
+
+
+These notebooks import functions from:
+
++ *_data*: contains functions to load flow data, calendar data and weather data 
++ *_features*: contains functions to add date features (based on calendar data) and to encode cyclical features (e.g. hours 1 to 24)
++ *_predictions*: contains functions for reproducing the predictions
++ *_training*: creates data_loaders
+
+then load preprocessed water demand data from the folder *processed_data* and weather data from the folder *weather*. Using the models in the folder *pre-trained_models* to reproduce our forecasting results.
 
 ## Funding