fixed typo

_chapters/convnets.md

@@ -150,7 +150,7 @@ Let's zoom out from what we just looked at and see the bigger picture. From this
 
 In this sense, we can think of the original image as a volume of data. Let's consider the previous example. Our original image is 28 x 28 pixels and is grayscale (1 channel). Thus it is a volume whose dimensions are 28x28x1. In the first convolutional layer, we convolved it with 8 filters whose dimensions are 5x5x1. This gave us 8 activation maps of size 24x24. Thus the output from the convolutional layer is size 24x24x8. After max-pooling it, it's 12x12x8. 
 
-What happens if the original image is color? In this case, our analogy scales very simply. Our convolutional filters would then also be color, and therefore have 3 channels. The convolution operation would work exactly as it did before, but simply have three times as many multiplications to make; the multiplications continue to line up by x and y as before, but also now by channel. So suppose we were using CIFAR-10 color images, whose size is 32x32x5, and we put it through a convolutional layer consisting of 20 filters of size 7x7x3. Then the output would be a volume of 26x26x20. The size in the x and y dimensions is 26 because there are 26x26 possible positions to slide a 7x7 filter into inside of a 32x32 image, and its depth is 20 because there are 20 filters.
+What happens if the original image is color? In this case, our analogy scales very simply. Our convolutional filters would then also be color, and therefore have 3 channels. The convolution operation would work exactly as it did before, but simply have three times as many multiplications to make; the multiplications continue to line up by x and y as before, but also now by channel. So suppose we were using CIFAR-10 color images, whose size is 32x32x3, and we put it through a convolutional layer consisting of 20 filters of size 7x7x3. Then the output would be a volume of 26x26x20. The size in the x and y dimensions is 26 because there are 26x26 possible positions to slide a 7x7 filter into inside of a 32x32 image, and its depth is 20 because there are 20 filters.
 
 {% include figure_multi.md path1="/images/figures/cnn_volumes.jpg" caption1="Volumes (source: <a href=\"https://cs231n.github.io/convolutional-networks/\">CS231n</a>)" %}