Update README.md

README.md

@@ -50,19 +50,6 @@ $$
 \mathcal{L} = \alpha \cdot \mathcal{L}_f + (1 - \alpha) \cdot \mathcal{L}_r
 $$
 
-where:
-
-$$
-\mathcal{L}_f = - \sum_{i \in \mathcal{D}_f} \log p(y_i | x_i, \theta)
-$$
-
-$$
-\mathcal{L}_r = \sum_{j \in \mathcal{D}_r} \log p(y_j | x_j, \theta)
-$$
-
-- $$\( \mathcal{D}_f \)$$ is the forget dataset.
-- $$\( \mathcal{D}_r \)$$ is the retain dataset.
-- $$\( \alpha \)$$ controls the balance between forgetting and retaining.
 
 ### Gradient Update: