diff --git a/notebooks/cv_gradient_boosting_tree.livemd b/notebooks/cv_gradient_boosting_tree.livemd
index bf475bea..5e6c41ce 100644
--- a/notebooks/cv_gradient_boosting_tree.livemd
+++ b/notebooks/cv_gradient_boosting_tree.livemd
@@ -22,6 +22,13 @@ require Explorer.DataFrame, as: DF
 require Explorer.Series, as: S
 ```
 
+And let's configure `EXLA` as our default backend (where our tensors are stored) and compiler (which compiles Scholar code) across the notebook and all branched sections:
+
+```elixir
+Nx.global_default_backend(EXLA.Backend)
+Nx.Defn.global_default_options(compiler: EXLA)
+```
+
 We are going to work with [Medical Cost Personal Datasets](https://www.kaggle.com/datasets/mirichoi0218/insurance) to predict medical charges that were applied to each person from the dataset. Let's download it:
 
 ```elixir