Update README.md

README.md

@@ -85,7 +85,7 @@ np.save("train_y.npy", train_y)
 
 The basic premise of how this network is trained and thus how the dataset is generated in the C program is:
 1. All models are scaled to a normal cubic scale and then scaled again by 0.55 so that they all fit within a unit sphere.
-2. All model vertices are reverse traced from the vertex position to the perimeter of the unit sphere using the vectex normal.
+2. All model vertices are reverse traced from the vertex position to the perimeter of the unit sphere using the vertex normal.
 3. The nearest position on a 10,242 vertex icosphere is found and the network is trained to output the model vertex position and vertex color (6 components) at the index of the icosphere vertex.
 4. The icosphere vertex index is scaled to a 0-1 range before being input to the network.
 5. The network only has two input parameters, the other parameter is a 0-1 model ID which is randomly selected and all vertices for a specific model are trained into the network using the randomly selected ID.