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diff --git a/tutorials/module_3/2_roots_optimization.md b/tutorials/module_3/2_roots_optimization.md
index 83684d5..cc9dbb5 100644
--- a/tutorials/module_3/2_roots_optimization.md
+++ b/tutorials/module_3/2_roots_optimization.md
@@ -59,8 +59,8 @@ Let's consider a continuous function $f(x)$ with an unknown root $x_r$ . Using t
 
 Once we bisect the interval and found we set the new predicted root to be in the middle. We can then compare the two sections and see if there is a sign change between the bounds. Once the section with the sign change has been identified, we can repeat this process until we near the root.
 
-![[Pasted image 20250905120647.png|500]]
-As you the figure shows, the predicted root $x_r$ get's closer to the actual root each iteration. In theory this is an infinite process that can keep on going. In practice, computer precision may cause error in the result. A work-around to these problems is setting a tolerance for the accuracy. As engineers it is our duty to determine what the allowable deviation is.
+![[bisection.png|500]]
+As you the figure shows, the predicted root $x_r$ get's closer to the actual root each iteration. In theory this is an infinite process that ca3n keep on going. In practice, computer precision may cause error in the result. A work-around to these problems is setting a tolerance for the accuracy. As engineers it is our duty to determine what the allowable deviation is.
 
 So let's take a look at how we can write this in python.
 ```python
diff --git a/tutorials/module_3/bisection.png b/tutorials/module_3/bisection.png
new file mode 100644
index 0000000..e561962
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+++ b/tutorials/module_3/bisection.png