Fix typo

convolutional-networks.md

@@ -97,7 +97,7 @@ The Conv layer is the core building block of a Convolutional Network, and its ou
 
 **Spatial arrangement**. We have explained the connectivity of each neuron in the Conv Layer to the input volume, but we haven't yet discussed how many neurons there are in the output volume or how they are arranged. Three hyperparameters control the size of the output volume: the **depth, stride** and **zero-padding**. We discuss these next:
 
-1. First, the **depth** of the output volume is a hyperparameter that we can pick; It controls the number of neurons in the Conv layer that connect to the same region of the input volume. This is analogous to a regular Neural Network, where we had multiple neurons in a hidden layer all looking at the exact same input. As we will see, all of these neurons will learn to activate for different features in the input. For example, if the first Convolutional Layer takes as input the raw image, then different neurons along the depth dimension may activate in presence of various oriented edged, or blobs of color. We will refer to a set of neurons that are all looking at the same region of the input as a **depth column**.
+1. First, the **depth** of the output volume is a hyperparameter that we can pick; It controls the number of neurons in the Conv layer that connect to the same region of the input volume. This is analogous to a regular Neural Network, where we had multiple neurons in a hidden layer all looking at the exact same input. As we will see, all of these neurons will learn to activate for different features in the input. For example, if the first Convolutional Layer takes as input the raw image, then different neurons along the depth dimension may activate in presence of various oriented edges, or blobs of color. We will refer to a set of neurons that are all looking at the same region of the input as a **depth column**.
 2. Second, we must specify the **stride** with which we allocate depth columns around the spatial dimensions (width and height). When the stride is 1, then we will allocate a new depth column of neurons to spatial positions only 1 spatial unit apart. This will lead to heavily overlapping receptive fields between the columns, and also to large output volumes. Conversely, if we use higher strides then the receptive fields will overlap less and the resulting output volume will have smaller dimensions spatially.
 3. As we will soon see, sometimes it will be convenient to pad the input with zeros spatially on the border of the input volume. The size of this **zero-padding** is a hyperparameter. The nice feature of zero padding is that it will allow us to control the spatial size of the output volumes. In particular, we will sometimes want to exactly preserve the spatial size of the input volume.