addressed dmitry's comments

hyperparameter_tuning/CNN/README.md

@@ -1,3 +1,3 @@
-# Multi-step forecasting with recurrent neural network that generates vector output
+# Multi-step forecasting with convolutional neural network
 
-The hyperparameters of recurrent neural network are tuned using Hyperdrive, a feature of Azure Machine Learning (Azure ML) service. To run this code, open and run the [hyperparameter_tuning.ipynb](../hyperparameter_tuning.ipynb) notebook, and specify [rnn_multistep_config.json](../rnn_multistep_config.json) as the configuration file.
+The hyperparameters of convolutional neural network are tuned using Hyperdrive, a feature of Azure Machine Learning (Azure ML) service. To run this code, open and run the [hyperparameter_tuning.ipynb](../hyperparameter_tuning.ipynb) notebook, and specify [cnn_config.json](../cnn_config.json) as the configuration file.

hyperparameter_tuning/README.md

@@ -24,12 +24,13 @@ The hyper-parameters of methods presented in this tutorial are tuned using Hyper
 ### Run hyper-parameter tuning notebook
 
 Hyper-parameter tuning is done in [hyperparameter_tuning.ipynb](./hyperparameter_tuning.ipynb) notebook. This notebook is used to tune several approaches:
-- Feed-forward network multi-step multivariate approach - [ff_multistep_config.json](ff_multistep_config.json)
+- Feed-forward network multi-step multivariate - [ff_multistep_config.json](ff_multistep_config.json)
-- RNN multi-step approach - [rnn_multistep_config.json](rnn_multistep_config.json)
+- RNN multi-step - [rnn_multistep_config.json](rnn_multistep_config.json)
-- RNN teacher forcing approach - [rnn_teacher_forcing_config.json](rnn_teacher_forcing_config.json)
+- RNN teacher forcing - [rnn_teacher_forcing_config.json](rnn_teacher_forcing_config.json)
-- RNN encoder decoder approach - [rnn_encoder_decoder_config.json](rnn_encoder_decoder_config.json)
+- RNN encoder decoder - [rnn_encoder_decoder_config.json](rnn_encoder_decoder_config.json)
+- CNN - [cnn_config.json](cnn_config.json)
 
 Each of these use cases is defined in a json configuration file listed above alongside each usecase. To run a specific approach, please specify the appropriate configuration file in the hyperparameter_tuning notebook.
 
 
-The running time depends on the size of your Azure ML cluster and the method being tuned.
+The running time depends on the size of your Azure ML cluster and the model being tuned.

hyperparameter_tuning/configuration.ipynb

@@ -80,6 +80,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
+    "scrolled": true,
     "tags": [
      "install"
     ]

hyperparameter_tuning/environment.yaml

@@ -17,7 +17,7 @@ dependencies:
 - ipykernel>=4.6.1
 - jupyter>=1.0.0
 - pip:
-  - azureml-sdk
+  - azureml-sdk==1.0.39
   - azureml-widgets
   - keras
   - numpy==1.16.3