updated readme

README.md

@@ -7,7 +7,7 @@ As part of this assignment, implement in PyTorch a minGRU architecture following
 We perform a first exploration of the potential of the recently-introduced minGRU models for parameter inference and forecasting in time-series. (The finer details of the results can be added after the results are finalised). (More detailed assignment description to be provided later.)
 ## Dataset Information
-The dataset required for this project is generated programmatically. Therefore, there is no need to download any external datasets.
+The dataset required for this project is generated programmatically. Therefore, there is no need to download any external datasets. (As of the current state of the project)
 ## Requirements
 To run this project, you will need the following dependencies:

milestone.md

@@ -60,6 +60,7 @@ The 4-layer model achieved the best overall perforamnce, followed by the 8-layer
 ### minGRU on parameter inference of sinusoidal waves (with noise)
 - Noise standard deviation: 0.01
 - Number of layers: 4
+
 Other hyperparameters stayed the same.
 - Training Loss: 0.14