Update tutorial.md (#679)

* Update tutorial.md

Added an explanation how to properly reset parameters + replaced "log-loss" with "log loss", to make it consistent with how it's spelled in Anki itself.

* Update tutorial.md

docs/tutorial.md

@@ -130,16 +130,20 @@ An option to optimize all presets has been added in Anki 23.12, it's useful if y
 
 ![image](https://github.com/open-spaced-repetition/fsrs4anki/assets/83031600/a5b930b9-2284-46c6-b98d-43c50215f6b3)
 
+If you ever want to reset parameters to their default values, click the anticlockwise open circle arrow to the right and bottom of the field with parameters.
+
+![Resetting parameters](https://github.com/user-attachments/assets/80832f4a-12c3-4e8b-adf3-898d9ab7a586)
+
 
 ## Step 4: (optional) Evaluate the parameters
 
 You can use the "Evaluate" button in the "Optimize FSRS parameters" section to see metrics that tell how well the parameters in the "FSRS parameters" field fit your review history. Smaller numbers indicate a better fit to your review history.
 
 ![image](https://github.com/open-spaced-repetition/fsrs4anki/assets/32575846/871bbe4d-8b05-4439-ab38-cf5c4e9f6fdf)
 
-Log-loss doesn't have an intuitive interpretation. RMSE (bins) can be interpreted as the average difference between the predicted probability of recalling a card (R) and the measured (from the review history) probability. For example, RMSE=0.05 means that, on average, FSRS is off by 5% when predicting R.
+Log loss doesn't have an intuitive interpretation. RMSE (bins) can be interpreted as the average difference between the predicted probability of recalling a card (R) and the measured (from the review history) probability. For example, RMSE=0.05 means that, on average, FSRS is off by 5% when predicting R.
 
-Note that log-loss and RMSE (bins) are not perfectly correlated, so two decks may have similar RMSE values but very different log-loss values, and vice-versa.
+Note that log loss and RMSE (bins) are not perfectly correlated, so two decks may have similar RMSE values but very different log loss values, and vice-versa.
 
 ## Step 5: (optional) Compute minimum recommended retention