typo

samples/core/tutorials/keras/basic_regression.ipynb

@@ -115,7 +115,7 @@
       },
       "cell_type": "markdown",
       "source": [
-        "In a *regression* problem, we aim to predict the output of a continous value, like a price or a probability. Contrast this with a *classification* problem, where we aim to predict a discrete label (for example, where a picture contains an apple or an orange). \n",
+        "In a *regression* problem, we aim to predict the output of a continuous value, like a price or a probability. Contrast this with a *classification* problem, where we aim to predict a discrete label (for example, where a picture contains an apple or an orange). \n",
         "\n",
         "This notebook builds a model to predict the median price of homes in a Boston suburb during the mid-1970s. To do this, we'll provide the model with some data points about the suburb, such as the crime rate and the local property tax rate.\n",
         "\n",
@@ -342,7 +342,7 @@
       "source": [
         "## Create the model\n",
         "\n",
-        "Let's build our model. Here, we'll use a `Sequential` model with two densely connected hidden layers, and an output later that returns a single, continous value. The model building steps are wrapped in a function, `build_model`, since we'll create a second model, later on."
+        "Let's build our model. Here, we'll use a `Sequential` model with two densely connected hidden layers, and an output later that returns a single, continuous value. The model building steps are wrapped in a function, `build_model`, since we'll create a second model, later on."
       ]
     },
     {