diff --git a/mini_proj/report/waldo.tex b/mini_proj/report/waldo.tex
index 37ac784..9390e02 100644
--- a/mini_proj/report/waldo.tex
+++ b/mini_proj/report/waldo.tex
@@ -260,7 +260,7 @@
 		To evaluate the performance of the models, we record the time taken by
 		each model to train, based on the training data and the accuracy with which
     the model makes predictions. We calculate accuracy as
-		\(a = \frac{|correct\ predictions|}{|predictions|} = \frac{tp + tn}{tp + tn + fp + fn}\)
+		\[a = \frac{|correct\ predictions|}{|predictions|} = \frac{tp + tn}{tp + tn + fp + fn}\]
 		where \(tp\) is the number of true positives, \(tn\) is the number of true
     negatives, \(fp\) is the number of false positives, and \(tp\) is the number
     of false negatives.
@@ -299,11 +299,11 @@
 				network and traditional machine learning technique}
 			\label{tab:results}
 		\end{table}
-    
+
     We can see by the results that Deep Neural Networks outperform our benchmark
     classification models, although the time required to train these networks is
     significantly greater.
-    
+
 		\section{Conclusion} \label{sec:conclusion}
 
 		Image from the ``Where's Waldo?'' puzzle books are ideal images to test