Update how-to-auto-train-forecast.md

Author: cartacioS

articles/machine-learning/how-to-auto-train-forecast.md

@@ -46,7 +46,7 @@ Deep learning models have three intrinsic capabilities:
 1. They support multiple inputs and outputs
 1. They can automatically extract patterns in input data that spans over long sequences
 
-Given larger data, deep learning models, such as Microsoft's ForecastTCN, can improve the scores of the resulting model. To leverage ForecastTCN set `enable_dnn=True` in your AutoMLConfig or select the **Enable deep learning** setting in the Automated ML SDK and UI respectively.
+Given larger data, deep learning models, such as Microsoft's ForecastTCN, can improve the scores of the resulting model. Learn how to [configure your experiment for deep learning](#Configure-a-DNN-enable-Forecasting-experiment).
 
 Native time series learners are also provided as part of automated ML. Prophet works best with time series that have strong seasonal effects and several seasons of historical data. Prophet is accurate & fast, robust to outliers, missing data, and dramatic changes in your time series. 
 
@@ -185,6 +185,17 @@ See the [forecasting sample notebooks](https://github.com/Azure/MachineLearningN
 
 In order to leverage DNNs for forecasting, you will need to set the `enable_dnn` parameter in the AutoMLConfig to true. 
 
+```python
+automl_config = AutoMLConfig(task='forecasting',
+                             enable_dnn=True,
+                             ...
+                             **time_series_settings)
+```
+Learn more about [the AutoMLConfig](#Configure-and-run-experiment).
+
+Alternatively, you can select the `Enable deep learning` option in the studio.
+![alt text](./media/how-to-auto-train-forecast/enable_dnn.png)
+
 We recommend using an AML Compute cluster with GPU SKUs and at least two nodes as the compute target. To allow sufficient time for the DNN training to complete, we recommend setting the experiment timeout to a minimum of a couple of hours.
 For more information on AML compute and VM sizes that include GPU's, see the [AML Compute documentation](how-to-set-up-training-targets.md#amlcompute) and [GPU optimized virtual machine sizes documentation](https://docs.microsoft.com/azure/virtual-machines/linux/sizes-gpu).