fix typo

src/pages/science/snow-detection.md

@@ -15,7 +15,7 @@ First, the images needed to be preprocessed and transformed. One problem snow de
 
 Our goal was to create a machine learning model that could detect whether there was snow on the ground around the river. Convolutional neural networks are the main tool of choice for these kinds of image related tasks. They work by using a sliding "window" across an image to capture relationships and patterns between pixels across the image. This sliding window approach reduces the number of parameters and complexity of the model. There are already a multitude of pre-trained convolutional network models out there that perform well on image classification tasks, but there aren't any deep learning models trained specifically for snow detection. _transfer learning_ comes to the rescue to make training a new model incredibly easy with limited time and computational power.
 
-Transfer learning works by taking an image classification model that someone else has already taken the time to train reusing it for a new purpose. We utilized ResNet50<sup>[[1](#references)]</sup>, a popular convolutional neural network model that pioneered a technique called residual connections. Residual connections allow neural networks to optimize quickly while still being deep enough to capture complex relationships. ResNet50 is a very deep network with fifty layers (hence the name) and would take a lot of time and computing power to train even with the residual connections, but some free pre-trained models are essentially plug-and-play with only small modifications. A visualization of ResNet50's architecture is seen below<sup>[[2](#references)]</sup.
+Transfer learning works by taking an image classification model that someone else has already taken the time to train reusing it for a new purpose. We utilized ResNet50<sup>[[1](#references)]</sup>, a popular convolutional neural network model that pioneered a technique called residual connections. Residual connections allow neural networks to optimize quickly while still being deep enough to capture complex relationships. ResNet50 is a very deep network with fifty layers (hence the name) and would take a lot of time and computing power to train even with the residual connections, but some free pre-trained models are essentially plug-and-play with only small modifications. A visualization of ResNet50's architecture is seen below<sup>[[2](#references)]</sup>.
 
 ![ResNet50 Model (without additional layers)](imgs/snow_ResNet50.png)