Merge branch 'erikreinertsen-patch-1'

Author: rasbt

faq/regularized-logistic-regression-performance.md

@@ -12,12 +12,12 @@ We know that our goal in an unregularized model is to minimize the cost function
 
 ![](./regularized-logistic-regression-performance/unregularized.png)
 
-Now, if we regularize the cost function (e.g., via L2 regularization), we add an additional to our cost function (J) that increases as the value of your parameter weights (w) increase; keep in mind that the regularization we add a new hyperparameter, lambda, to control the regularization strength.
+Now, if we regularize the cost function (e.g., via L2 regularization), we add an additional to our cost function (J) that increases with the value of your parameter weights (w). Regularization strength is controlled via the addition of a new hyperparameter, lambda.
 
 ![](./regularized-logistic-regression-performance/l2-term.png)
 
 Therefore, our new problem is to minimize the cost function given this added constraint.
 
 ![](./regularized-logistic-regression-performance/regularized.png)
 
-Intuitively, we can think of the "sphere" at the coordinate center in the figure above as our "budget." Now, our objective is still the same: we want to minimize the cost function. However, we are now constraint by the regularization term; we want to get as close as possible to the global minimum while staying within our "budget" (i.e., the sphere).
+Intuitively, we can think of the "sphere" at the coordinate center in the figure above as our "budget." Now, our objective is still the same: we want to minimize the cost function. However, we are now constrained by the regularization term; we want to get as close as possible to the global minimum while staying within our "budget" (i.e., the sphere).