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+# Training ML models with Azure ML SDK
+These notebook tutorials cover the various scenarios for training machine learning and deep learning models with Azure Machine Learning.
+
+## Sample notebooks
+- [01.train-hyperparameter-tune-deploy-with-pytorch](https://github.com/Azure/MachineLearningNotebooks/tree/master/training/01.train-hyperparameter-tune-deploy-with-pytorch)  
+Train, hyperparameter tune, and deploy a PyTorch image classification model that distinguishes bees vs. ants using transfer learning. Azure ML concepts covered:
+  - Create a remote compute target (Batch AI cluster)
+  - Upload training data using `Datastore`
+  - Run a single-node `PyTorch` training job
+  - Hyperparameter tune model with HyperDrive
+  - Find and register the best model
+  - Deploy model to ACI
+- [02.distributed-pytorch-with-horovod](https://github.com/Azure/MachineLearningNotebooks/tree/master/training/02.distributed-pytorch-with-horovod)  
+Train a PyTorch model on the MNIST dataset using distributed training with Horovod. Azure ML concepts covered:
+  - Create a remote compute target (Batch AI cluster)
+  - Run a two-node distributed `PyTorch` training job using Horovod
+- [03.train-hyperparameter-tun-deploy-with-tensorflow](https://github.com/Azure/MachineLearningNotebooks/tree/master/training/03.train-hyperparameter-tune-deploy-with-tensorflow)  
+Train, hyperparameter tune, and deploy a TensorFlow model on the MNIST dataset. Azure ML concepts covered:
+  - Create a remote compute target (Batch AI cluster)
+  - Upload training data using `Datastore`
+  - Run a single-node `TensorFlow` training job
+  - Leverage features of the `Run` object
+  - Download the trained model
+  - Hyperparameter tune model with HyperDrive
+  - Find and register the best model
+  - Deploy model to ACI
+- [04.distributed-tensorflow-with-horovod](https://github.com/Azure/MachineLearningNotebooks/tree/master/training/04.distributed-tensorflow-with-horovod)  
+Train a TensorFlow word2vec model using distributed training with Horovod. Azure ML concepts covered:
+  - Create a remote compute target (Batch AI cluster)
+  - Upload training data using `Datastore`
+  - Run a two-node distributed `TensorFlow` training job using Horovod
+- [05.distributed-tensorflow-with-parameter-server](https://github.com/Azure/MachineLearningNotebooks/tree/master/training/05.distributed-tensorflow-with-parameter-server)  
+Train a TensorFlow model on the MNIST dataset using native distributed TensorFlow (parameter server). Azure ML concepts covered:
+  - Create a remote compute target (Batch AI cluster)
+  - Run a two workers, one parameter server distributed `TensorFlow` training job
+- [06.distributed-cntk-with-custom-docker](https://github.com/Azure/MachineLearningNotebooks/tree/master/training/06.distributed-cntk-with-custom-docker)  
+Train a CNTK model on the MNIST dataset using the Azure ML base `Estimator` with custom Docker image and distributed training. Azure ML concepts covered:
+  - Create a remote compute target (Batch AI cluster)
+  - Upload training data using `Datastore`
+  - Run a base `Estimator` training job using a custom Docker image from Docker Hub
+  - Distributed CNTK two-node training job via MPI using base `Estimator`
+  
+- [07.tensorboard](https://github.com/Azure/MachineLearningNotebooks/tree/master/training/07.tensorboard)  
+Train a TensorFlow MNIST model locally, on a DSVM, and on Batch AI and view the logs live on TensorBoard. Azure ML concepts covered:
+  - Run the training job locally with Azure ML and run TensorBoard locally. Start (and stop) an Azure ML `TensorBoard` object to stream and view the logs
+  - Run the training job on a remote DSVM and stream the logs to TensorBoard
+  - Run the training job on a remote Batch AI cluster and stream the logs to TensorBoard
+  - Start a `Tensorboard` instance that displays the logs from all three above runs in one
+- [08.export-run-history-to-tensorboard](https://github.com/Azure/MachineLearningNotebooks/tree/master/training/08.export-run-history-to-tensorboard)
+  - Start an Azure ML `Experiment` and log metrics to `Run` history
+  - Export the `Run` history logs to TensorBoard logs
+  - View the logs in TensorBoard