Minor typo error (#203)

In line 119: 
Before: because that it is how many neurons
After: because that is how many neurons

Author: jin14

convolutional-networks.md

@@ -116,7 +116,7 @@ We can compute the spatial size of the output volume as a function of the input
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-*Use of zero-padding*. In the example above on left, note that the input dimension was 5 and the output dimension was equal: also 5. This worked out so because our receptive fields were 3 and we used zero padding of 1. If there was no zero-padding used, then the output volume would have had spatial dimension of only 3, because that it is how many neurons would have "fit" across the original input. In general, setting zero padding to be \\(P = (F - 1)/2\\) when the stride is \\(S = 1\\) ensures that the input volume and output volume will have the same size spatially. It is very common to use zero-padding in this way and we will discuss the full reasons when we talk more about ConvNet architectures.
+*Use of zero-padding*. In the example above on left, note that the input dimension was 5 and the output dimension was equal: also 5. This worked out so because our receptive fields were 3 and we used zero padding of 1. If there was no zero-padding used, then the output volume would have had spatial dimension of only 3, because that is how many neurons would have "fit" across the original input. In general, setting zero padding to be \\(P = (F - 1)/2\\) when the stride is \\(S = 1\\) ensures that the input volume and output volume will have the same size spatially. It is very common to use zero-padding in this way and we will discuss the full reasons when we talk more about ConvNet architectures.
 
 *Constraints on strides*. Note again that the spatial arrangement hyperparameters have mutual constraints. For example, when the input has size \\(W = 10\\), no zero-padding is used \\(P = 0\\), and the filter size is \\(F = 3\\), then it would be impossible to use stride \\(S = 2\\), since \\((W - F + 2P)/S + 1 = (10 - 3 + 0) / 2 + 1 = 4.5\\), i.e. not an integer, indicating that the neurons don't "fit" neatly and symmetrically across the input. Therefore, this setting of the hyperparameters is considered to be invalid, and a ConvNet library could throw an exception or zero pad the rest to make it fit, or crop the input to make it fit, or something. As we will see in the ConvNet architectures section, sizing the ConvNets appropriately so that all the dimensions "work out" can be a real headache, which the use of zero-padding and some design guidelines will significantly alleviate.