Model parameters are optimized using gradient descent with the ADAM optimizer, employing a learning rate of 10 -3 . The dataset is split into 80% training and 20% validation, and early stopping is implemented to mitigate overfitting and fluctuations caused by the skewness term. If the dataset size is not an exact multiple of the batch size, the last incomplete batch is discarded for simplicity.