adding readmes

Biomedical entity recognition/readme.md

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+# IoT Scenario - Biomedical Entity Extraction
+
+Biomedical named entity recognition is a critical step for complex biomedical NLP tasks such as:
+Extraction of diseases, symptoms from electronic medical or health records.
+Drug discovery
+Understanding the interactions between different entity types such as drug-drug interaction, drug-disease relationship and gene-protein relationship.
+Our use case scenario focuses on how a large amount of unstructured unlabeled data corpus such as PubMed article abstracts can be analyzed to train a domain-specific word embedding model. Then the output embeddings are considered as automatically generated features to train a neural entity extraction model using Keras with TensorFlow deep learning framework as backend and a small amoht of labeled data. 
+
+We have also included a Docker container with the final model. This container can be deployed to an IoT device via Azure IoT Hub.
+
+## Link to the Microsoft DOCS site
+
+The detailed documentation for this real world scenario includes the step-by-step walkthrough:
+
+[https://docs.microsoft.com/en-us/azure/machine-learning/preview/scenario-tdsp-biomedical-recognition](https://docs.microsoft.com/en-us/azure/machine-learning/preview/scenario-tdsp-biomedical-recognition)
+
+## Link to the Gallery GitHub repository
+
+The public GitHub repository for this real world scenario contains all the code samples:
+
+[https://github.com/Azure/MachineLearningSamples-BiomedicalEntityExtraction](https://github.com/Azure/MachineLearningSamples-BiomedicalEntityExtraction)
+

Energy demand time series/readme.md

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+# IoT Scenario - Energy Demand Time Series Forecasting
+
+Time series forecasting is the task of predicting future values in a time-ordered sequence of observations. It is a common problem and has applications in many industries. This example focuses on energy demand forecasting where the goal is to predict the future load on an energy grid. Although the context is energy demand forecasting, the methods used can be applied to many other contexts and use cases. For example, package delivery companies need to estimate the demand for their services so they can plan workforce requirements and delivery routes ahead of time. In many cases, the financial risks of inaccurate forecasts can be significant. Therefore, forecasting is often a business critical activity.
+
+This sample shows how time series forecasting can be performed through applying machine learning techniques. You are guided through every step of the modeling process. We have also included a Docker container with the final model. This container can be deployed to an IoT device via Azure IoT Hub. 
+
+## Link to the Microsoft DOCS site
+
+The detailed documentation for this real world scenario includes the step-by-step walkthrough:
+
+[https://docs.microsoft.com/azure/machine-learning/preview/scenario-time-series-forecasting](https://docs.microsoft.com/azure/machine-learning/preview/scenario-time-series-forecasting)
+
+## Link to the Gallery GitHub repository
+
+The public GitHub repository for this real world scenario contains all the code samples:
+
+[https://github.com/Azure/MachineLearningSamples-EnergyDemandTimeSeriesForecasting](https://github.com/Azure/MachineLearningSamples-EnergyDemandTimeSeriesForecasting)
+
+

Image classification using CNTK/readme.md

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+# IoT Scenario: Image Classification using Microsoft Cognitive Toolkit (CNTK)
+
+This sample uses the popular [TensorFlow](https://www.tensorflow.org/) machine learning library from Google to classify the ageless [MNIST dataset](http://yann.lecun.com/exdb/mnist/) of handwritten digits. The project has been specifically designed to run with the Azure Machine Learning Workbench.
+
+
+## Link to the Gallery GitHub repository
+
+The public GitHub repository for this image classification example contains all the code samples:
+[https://github.com/Azure/MachineLearningSamples-tf](https://github.com/Azure/MachineLearningSamples-tf)
+
+
+

LICENSE

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     LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
     OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
     SOFTWARE
+

Predictive maintenance/readme.md

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+# IoT Scenario: General Predictive Maintenance
+
+This scenario uses relatively large-scale data to walk the user through the main steps of an IoT AI project -- from data ingestion, feature engineering, model building, and then finally model operationalization and deployment. The code for the entire process is written in PySpark and implemented using Jupyter notebooks within Azure ML Workbench. The included Docker container can be deployed directly to an IoT device using Azure IoT Hub.    
+
+The detailed documentation for this real world scenario includes the step-by-step walk-through:
+[https://docs.microsoft.com/azure/machine-learning/preview/scenario-predictive-maintenance](https://docs.microsoft.com/azure/machine-learning/preview/scenario-predictive-maintenance)
+
+The public GitHub repository for this real world scenario contains all the code samples:
+[https://github.com/Azure/MachineLearningSamples-PredictiveMaintenance](https://github.com/Azure/MachineLearningSamples-PredictiveMaintenance)