fixed punctation [ci skip]

Author: marscher

notebooks/02-dimension-reduction-and-discretization.ipynb

@@ -425,7 +425,7 @@
    "metadata": {},
    "source": [
     "### Streaming memory discretization\n",
-    "For real world case examples it is often not possible to load entire datasets into main memory. We can perform the whole discretization step without the need of having the dataset fit into memory. Keep in mind that this is not as efficient as loading into memory, because certain things (eg. featurization) will have to be recomputed during iterations."
+    "For real world case examples it is often not possible to load entire datasets into main memory. We can perform the whole discretization step without the need of having the dataset fit into memory. Keep in mind that this is not as efficient as loading into memory, because certain calculations (e.g. featurization), will have to be recomputed during iterations."
    ]
   },
   {
@@ -447,7 +447,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We should mention that regular space clustering does not require to load the TICA output into memory, while $k$-means does. Use the minibatch version, if your TICA output does not fit memory. Since the minibatch version takes more time to converge, it is therefore desirable to to shrink the TICA output to fit into memory. We split the pipeline for cluster estimation, and re-use the reader to for the assignment of the full dataset."
+    "We should mention that regular space clustering does not require to load the TICA output into memory, while $k$-means does. Use the minibatch version if your TICA output does not fit memory. Since the minibatch version takes more time to converge, it is therefore desirable to to shrink the TICA output to fit into memory. We split the pipeline for cluster estimation, and re-use the reader to for the assignment of the full dataset."
    ]
   },
   {