1 files changed, 10 insertions, 1 deletions
diff --git a/Fundamentals_of_Accelerated_Data_Science/3-04_logistic_regression.ipynb b/Fundamentals_of_Accelerated_Data_Science/3-04_logistic_regression.ipynb
index 3890ea7..4e4b2ab 100644
--- a/Fundamentals_of_Accelerated_Data_Science/3-04_logistic_regression.ipynb
+++ b/Fundamentals_of_Accelerated_Data_Science/3-04_logistic_regression.ipynb
@@ -205,7 +205,9 @@
     "## Logistic Regression ##\n",
     "Logistic regression can be used to estimate the probability of an outcome as a function of some (assumed independent) inputs. In our case, we would like to estimate infection risk based on population members' age and sex.\n",
     "\n",
-    "Below we train a logistic regresion model. We first create a cuML logistic regression instance `logreg`. The `logreg.fit` method takes 2 arguments: the model's independent variables *X*, and the dependent variable *y*. Fit the `logreg` model using the `gdf` columns `age` and `sex` as *X* and the `infected` column as *y*."
+    "Below we train a logistic regresion model. We first create a cuML logistic regression instance `logreg`. The `logreg.fit` method takes 2 arguments: the model's independent variables *X*, and the dependent variable *y*. Fit the `logreg` model using the `gdf` columns `age` and `sex` as *X* and the `infected` column as *y*.\n",
+    "\n",
+    "1/(1+e^{-z}) sigmoid"
    ]
   },
   {
@@ -637,6 +639,13 @@
    ]
   },
   {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
    "cell_type": "markdown",
    "metadata": {},
    "source": [