Fix links in notebooks

Author: basnijholt

docs/source/algorithms_and_examples.md

@@ -84,7 +84,6 @@ runner.live_plot()
 ```{code-cell} ipython3
 :tags: [hide-cell]
 
-
 def f(x, offset=0.07357338543088588):
     a = 0.01
     return x + a**2 / (a**2 + (x - offset) ** 2)
@@ -110,11 +109,9 @@ def get_hm(loss_per_interval, N=101):
     plots = {n: plot(learner, n) for n in range(N)}
     return hv.HoloMap(plots, kdims=["npoints"])
 
-
-layout = get_hm(uniform_loss).relabel("homogeneous samping") + get_hm(
-    default_loss
-).relabel("with adaptive")
-
+plot_homo = get_hm(uniform_loss).relabel("homogeneous samping")
+plot_adaptive = get_hm(default_loss).relabel("with adaptive")
+layout = plot_homo + plot_adaptive
 layout.opts(plot=dict(toolbar=None))
 ```
 

docs/source/tutorial/tutorial.custom_loss.md

@@ -29,7 +29,7 @@ from functools import partial
 import holoviews as hv
 ```
 
-`~adaptive.Learner1D` and {class}`~adaptive.Learner2D` both work on the principle of subdividing their domain into subdomains, and assigning a property to each subdomain, which we call the *loss*.
+{class}`~adaptive.Learner1D` and {class}`~adaptive.Learner2D` both work on the principle of subdividing their domain into subdomains, and assigning a property to each subdomain, which we call the *loss*.
 The algorithm for choosing the best place to evaluate our function is then simply *take the subdomain with the largest loss and add a point in the center, creating new subdomains around this point*.
 
 The *loss function* that defines the loss per subdomain is the canonical place to define what regions of the domain are “interesting”.
@@ -164,4 +164,4 @@ Awesome! We zoom in on the singularity, but not at the expense of sampling the r
 
 The above strategy is available as {class}`adaptive.learner.learner2D.resolution_loss_function`.
 
-[^download]: This notebook can be downloaded as **{nb-download}`tutorial.custom-loss.ipynb`** and {download}`tutorial.custom-loss.md`.
+[^download]: This notebook can be downloaded as **{nb-download}`tutorial.custom_loss.ipynb`** and {download}`tutorial.custom_loss.md`.