update

doc/src/week41/week41.do.txt

@@ -1120,6 +1120,7 @@ Our design matrix is defined by the input values $x_1$ and $x_2$. Since we have
 		       1 & 0 \\
 		       1 & 1 \end{bmatrix},
 !et
+
 while the vector of outputs is $\bm{y}^T=[0,1,1,0]$ for the XOR gate, $\bm{y}^T=[0,0,0,1]$ for the AND gate and $\bm{y}^T=[0,1,1,1]$ for the OR gate.
 
 !split
@@ -1195,13 +1196,13 @@ predictions = predict(X)
 print(predictions)
 
 !ec
+
 Not an impressive result, but this was our first forward pass with randomly assigned weights. Let us now add the full network with the back-propagation algorithm discussed above.
 
 !split
 ===== The Code using Scikit-Learn =====
 
 !bc pycod
-
 # import necessary packages
 import numpy as np
 import matplotlib.pyplot as plt