Update neural-networks-2.md

Author: infinnie

neural-networks-2.md

@@ -55,7 +55,7 @@ where the columns of `U` are the eigenvectors and `S` is a 1-D array of the sing
 Xrot = np.dot(X, U) # decorrelate the data
 ```
 
-Notice that the columns of `U` are a set of orthonormal vectors (norm of 1, and orthogonal to each other), so they can be regarded as basis vectors. The projection therefore corresponds to a rotation of the data in `X` so that the new axes are the eigenvectors. If we were to compute the covariance matrix of `Xrot`, we would see that it is now diagonal. A nice property of `np.linalg.svd` is that in its returned value `U`, the eigenvector columns are sorted by their eigenvalues. We can use this to reduce the dimensionality of the data by only using the top few eigenvectors, and discarding the dimensions along which the data has no variance. This is also sometimes refereed to as [Principal Component Analysis (PCA)](http://en.wikipedia.org/wiki/Principal_component_analysis) dimensionality reduction:
+Notice that the columns of `U` are a set of orthonormal vectors (norm of 1, and orthogonal to each other), so they can be regarded as basis vectors. The projection therefore corresponds to a rotation of the data in `X` so that the new axes are the eigenvectors. If we were to compute the covariance matrix of `Xrot`, we would see that it is now diagonal. A nice property of `np.linalg.svd` is that in its returned value `U`, the eigenvector columns are sorted by their eigenvalues. We can use this to reduce the dimensionality of the data by only using the top few eigenvectors, and discarding the dimensions along which the data has no variance. This is also sometimes referred to as [Principal Component Analysis (PCA)](http://en.wikipedia.org/wiki/Principal_component_analysis) dimensionality reduction:
 
 ```python
 Xrot_reduced = np.dot(X, U[:,:100]) # Xrot_reduced becomes [N x 100]