fixed emojis

docs/how_to_guide/plot_05_sklearn_pipeline_cv_demo.md

@@ -438,7 +438,8 @@ if path.exists():
   fig.savefig(path / "plot_05_sklearn_pipeline_cv_demo.svg")
 ```
 
-_U+1F680 _U+1F680 _U+1F680 **Success!** _U+1F680 _U+1F680 _U+1F680
+🚀🚀🚀 **Success!** 🚀🚀🚀
+
 We are now able to capture the distribution of the firing rate appropriately: both peaks and valleys in the spiking activity are matched by our model predicitons.
 
 ### Evaluating different bases directly

docs/tutorials/plot_01_current_injection.md

@@ -444,14 +444,14 @@ following properties:
   arrays, `numpy` arrays or `pynapple` `TsdFrame`/`Tsd`.
 
 :::{admonition} What is jax?
-:class: info
-
-  [jax](https://github.com/google/jax) is a Google-supported python library
-  for automatic differentiation. It has all sorts of neat features, but the
-  most relevant of which for NeMoS is its GPU-compatibility and
-  just-in-time compilation (both of which make code faster with little
-  overhead!), as well as the collection of optimizers present in
-  [jaxopt](https://jaxopt.github.io/stable/).
+:class: note
+
+[jax](https://github.com/google/jax) is a Google-supported python library
+for automatic differentiation. It has all sorts of neat features, but the
+most relevant of which for NeMoS is its GPU-compatibility and
+just-in-time compilation (both of which make code faster with little
+overhead!), as well as the collection of optimizers present in
+[jaxopt](https://jaxopt.github.io/stable/).
 :::
 
 First, we require that our predictors and our spike counts have the same