Update app.py

app.py

@@ -78,5 +78,6 @@ st.markdown(r"""
            Here are some observations to note while experimenting with the hyperparameters:
            * Lengthscale $\ell$ controls the smoothness of the fit. Smoothness in fit increases with an increase in $\ell$.
            * Variance $\sigma_f^2$ controls the uncertainty in the model (aka epistemic uncertainty). Sometimes it is also called lengthscale in the vertical direction [[Slide 154](http://cbl.eng.cam.ac.uk/pub/Public/Turner/News/imperial-gp-tutorial.pdf)]).
-           * Noise variance $\sigma_n^2$ is a measure of observation noise or irreducible noise (aka aleatoric uncertainty) present in the dataset. Increasing noise variance to a certain limit reduces overfitting. One can fix it if known from the data generation process or can be learned during the hyperparameter optimization process.
+           * Noise variance $\sigma_n^2$ is a measure of observation noise or irreducible noise (aka aleatoric uncertainty) present in the dataset. Increasing noise variance to a certain limit reduces overfitting. One can fix it if known from the data generation process or it can be learned during the hyperparameter optimization process.
+           * Negative Log Marginal Likelihood works as a loss function for GP hyperparameter tuning. Though there are advanced tools available for hyperparameter tuning, you can manually optimize them with the sliders above to test your understanding. 
            """)