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@@ -182,7 +182,7 @@ $$
 
 Where \\(N\\) is the number of training examples. As you can see, we append the regularization penalty to the loss objective, weighted by a hyperparameter \\(\lambda\\). There is no simple way of setting this hyperparameter and it is usually determined by cross-validation.
 
-In addition to the motivation we provided above there are many desirable properties to include the regularization penalty, many of which we will come back to in later sections. For example, it turns out that including the L2 penalty leads to the appealing **max margin** property in SVMs (See [CS229](http://cs229.stanford.edu/notes/cs229-notes3.pdf) lecture notes for full details if you are interested).
+In addition to the motivation we provided above there are many desirable properties to include the regularization penalty, many of which we will come back to in later sections. For example, it turns out that including the L2 penalty leads to the appealing **max margin** property in SVMs (See [CS229](https://see.stanford.edu/materials/aimlcs229/cs229-notes3.pdf) lecture notes for full details if you are interested).
 
 The most appealing property is that penalizing large weights tends to improve generalization, because it means that no input dimension can have a very large influence on the scores all by itself. For example, suppose that we have some input vector \\(x = [1,1,1,1] \\) and two weight vectors \\(w_1 = [1,0,0,0]\\), \\(w_2 = [0.25,0.25,0.25,0.25] \\). Then \\(w_1^Tx = w_2^Tx = 1\\) so both weight vectors lead to the same dot product, but the L2 penalty of \\(w_1\\) is 1.0 while the L2 penalty of \\(w_2\\) is only 0.25. Therefore, according to the L2 penalty the weight vector \\(w_2\\) would be preferred since it achieves a lower regularization loss. Intuitively, this is because the weights in \\(w_2\\) are smaller and more diffuse. Since the L2 penalty prefers smaller and more diffuse weight vectors, the final classifier is encouraged to take into account all input dimensions to small amounts rather than a few input dimensions and very strongly. As we will see later in the class, this effect can improve the generalization performance of the classifiers on test images and lead to less *overfitting*.