diff --git a/optimizations.qmd b/optimizations.qmd
index cb38f24f..02c33084 100644
--- a/optimizations.qmd
+++ b/optimizations.qmd
@@ -506,7 +506,7 @@ Entropy: Use KL divergence to minimize information loss between the original flo
 
 Percentile: Set the range to a percentile of the distribution of absolute values seen during calibration. For example, 99% calibration would clip 1% of the largest magnitude values.
 
-![Histogram of input activatsions to layer 3 in ResNet50 and calibrated ranges (@intquantfordeepinf).](images/efficientnumerics_calibrationcopy.png)
+![Histogram of input activations to layer 3 in ResNet50 and calibrated ranges (@intquantfordeepinf).](images/efficientnumerics_calibrationcopy.png)
 
 Importantly, the quality of calibration can make a difference between a quantized model that retains most of its accuracy and one that degrades significantly. Hence, it's an essential step in the quantization process. When choosing a calibration range, there are two types: symmetric and asymmetric.