datascibox
diff --git a/‎tutorials/multiprocessing_intro.ipynb‎
Lines changed: 5 additions & 5 deletions b/‎tutorials/multiprocessing_intro.ipynb‎
Lines changed: 5 additions & 5 deletions
@@ -348,7 +348,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**A simpler way to to maintain an ordered list of results is to use the `Pool.apply` and `Pool.map` functions which we will discuss in the next section.**"
+    "**A simpler way to maintain an ordered list of results is to use the `Pool.apply` and `Pool.map` functions which we will discuss in the next section.**"
    ]
   },
   {
@@ -379,7 +379,7 @@
    "source": [
     "Another and more convenient approach for simple parallel processing tasks is provided by the `Pool` class.  \n",
     "\n",
-    "There are four methods that are particularly interesing:\n",
+    "There are four methods that are particularly interesting:\n",
     "\n",
     "    - Pool.apply\n",
     "    \n",
@@ -451,7 +451,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The `Pool.map` and `Pool.apply` will lock the main program until all a process is finished, which is quite useful if we want to obtain resuls in a particular order for certain applications.   \n",
+    "The `Pool.map` and `Pool.apply` will lock the main program until all processes are finished, which is quite useful if we want to obtain results in a particular order for certain applications.   \n",
     "In contrast, the `async` variants will submit all processes at once and retrieve the results as soon as they are finished. \n",
     "One more difference is that we need to use the `get` method after the `apply_async()` call in order to obtain the `return` values of the finished processes."
    ]
@@ -759,7 +759,7 @@
    "source": [
     "Below, we will set up benchmarking functions for our serial and multiprocessing approach that we can pass to our `timeit` benchmark function.  \n",
     "We will be using the `Pool.apply_async` function to take advantage of firing up processes simultaneously: Here, we don't care about the order in which the results for the different window widths are computed, we just need to associate each result with the input window width.  \n",
-    "Thus we add a little tweak to our Parzen-density-estimation function by returning a tuple of 2 values: window width and the estimated density, which will allow us to to sort our list of results later."
+    "Thus we add a little tweak to our Parzen-density-estimation function by returning a tuple of 2 values: window width and the estimated density, which will allow us to sort our list of results later."
    ]
   },
   {
@@ -1097,7 +1097,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.1"
+   "version": "3.6.3"
   }
  },
  "nbformat": 4,