Copy-edits from lamberta

samples/core/tutorials/estimators/linear.ipynb

@@ -1296,7 +1296,7 @@
       },
       "cell_type": "markdown",
       "source": [
-        "These regularized models don't perform very differently than the base model. Let's look ar the models' weight distributions to better see the effect of the regularization:"
+        "These regularized models don't perform much better than the base model. Let's look at the model's weight distributions to better see the effect of the regularization:"
       ]
     },
     {
@@ -1332,7 +1332,7 @@
       },
       "cell_type": "markdown",
       "source": [
-        "The models have many zero-vlaued weights  caused by unused hash bins (There are many more hash bins than categories in some columns). We will mask these weights when viewing the weight distributions:"
+        "The models have many zero-valued weights caused by unused hash bins (there are many more hash bins than categories in some columns). We can mask these weights when viewing the weight distributions:"
       ]
     },
     {
@@ -1396,7 +1396,7 @@
       },
       "cell_type": "markdown",
       "source": [
-        "Both types of regularization squeeze the distribution of weights towards zero. L2 regularization has a greater effect in the tails of the distribution eliminating extreme weights. L1 regularization produces more exactly-zero values (In this case it sets ~200 to zero)."
+        "Both types of regularization squeeze the distribution of weights towards zero. L2 regularization has a greater effect in the tails of the distribution eliminating extreme weights. L1 regularization produces more exactly-zero values, in this case it sets ~200 to zero."
       ]
     }
   ]