diff --git a/generating_random.md b/generating_random.md
index 1f81003..e44a302 100644
--- a/generating_random.md
+++ b/generating_random.md
@@ -87,12 +87,12 @@ $$0\leq u \leq \sqrt{f\left({v\over u}\right)}$$
 In this method, $f(x)$ doesn't need to be normalized, only the shape of the function is needed.  This method was introduced by [Kinderman and Monahan 1977](https://dl.acm.org/doi/pdf/10.1145/355744.355750). 
 Again note how this method allows us to access the range $x=0$ to $\infty$, since the ratio $v/u\rightarrow \infty$ for $u\rightarrow 0$. 
 
-```{admonition} Exercise:
+```{admonition} Exercise 1
 Implement these three methods for the exponential distribution and check that they work by comparing a histogram of your $x$ values with the analytic function.
 ```
 
 
-```{admonition} Exercise:
+```{admonition} Exercise 2
 Try implementing one of the following:
 
 - Lorentzian distribution using transformation method
diff --git a/prob_distributions_solutions.ipynb b/prob_distributions_solutions.ipynb
index b24e21b..e0ad496 100644
--- a/prob_distributions_solutions.ipynb
+++ b/prob_distributions_solutions.ipynb
@@ -5,7 +5,7 @@
    "id": "6d46a72f",
    "metadata": {},
    "source": [
-    "# Sampling probability distributions Part 1"
+    "# Probability distributions Exercise 1"
    ]
   },
   {
@@ -260,7 +260,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.11.5"
   }
  },
  "nbformat": 4,