conclusion

Author: Sandeep Kumar

samples/04_gis_analysts_data_scientists/training_a_wind_turbine_detection_model_using_large_volume_of_training_data.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Training a wind turbine detection model using large volume of training data"
+    "# Training a wind turbine detection model using large volumes of training data"
    ]
   },
   {
@@ -643,7 +643,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This notebook has demonstrated a workflow to train a deep learning model that can detect features representing wind turbines. This notebook shows how multiprocessing can be used to the reduce time required to export training data. In this notebook we also leveraged multiple GPUs to reduce the time required for model training. A similar approach can be applied to classify other objects of interest, like trees, buildings, structures, etc."
+    "This notebook has demonstrated a workflow to train a deep learning model that detects features representing wind turbines. This notebook uses multiprocessing to reduce the time required to export training data. In this notebook, we also leveraged multiple GPUs to reduce the time for model training. We can follow a similar approach to classify other objects of interest, like trees, buildings, structures, etc."
    ]
   },
   {