Update README.md

README.md

@@ -230,20 +230,20 @@ For those inclined towards the intricacies of the model, the specific parameters
 
 ### Loss function
 
-The Swin2 transformer optimizes its parameters using a composite loss function that aggregates multiple \( \mathcal{L}_1 \) loss terms to enhance its predictive 
+The Swin2 transformer optimizes its parameters using a composite loss function that aggregates multiple L1 loss terms to enhance its predictive 
 accuracy across different resolutions and representations:
 
 1. **Primary Predictions Loss**: 
-   - This term computes the \( \mathcal{L}_1 \) loss between the primary model predictions and the reference values. It ensures that the transformer's outputs
+   - This term computes the L1 loss between the primary model predictions and the reference values. It ensures that the transformer's outputs
    - closely match the ground truth across the primary spatial resolution.
 
 2. **Downsampled Predictions Loss**: 
-   - Recognizing the importance of accuracy across varying resolutions, this term calculates the \( \mathcal{L}_1 \) loss between the downsampled versions of the
+   - Recognizing the importance of accuracy across varying resolutions, this term calculates the L1 loss between the downsampled versions of the
    - predictions and the reference values. By incorporating this term, the model is incentivized to preserve critical information even when the data is represented
    - at a coarser scale.
 
 3. **Blurred Predictions Loss**: 
-   - To ensure the model's robustness against small perturbations and noise, this term evaluates the \( \mathcal{L}_1 \) loss between blurred versions of the
+   - To ensure the model's robustness against small perturbations and noise, this term evaluates the L1 loss between blurred versions of the
    - predictions and the references. This encourages the model to produce predictions that maintain accuracy even under slight modifications in the data representation.
 
 By combining these loss terms, the Swin2 transformer is trained to produce accurate predictions across different resolutions and under various data transformations,