Add release notes for ML.NET 0.5

docs/release-notes/0.5/release-0.5.md

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+# ML.NET 0.5 Release Notes
+
+Today we are excited to release ML.NET 0.5. This release adds
+[TensorFlow](https://www.tensorflow.org/) model scoring as a transform to
+ML.NET. This enables using an existing TensorFlow model within an ML.NET
+experiment. In addition to this, we have continued the work on new APIs that
+enable currently missing functionality. We welcome feedback and contributions
+to the conversation: relevant issues can be found
+[here](https://github.com/dotnet/machinelearning/projects/4). A simple example
+of the new APIs can be found
+[here](https://github.com/dotnet/machinelearning/blob/21b61447a342718c93f4b47ef8b5f2ec6d9f0c44/test/Microsoft.ML.Tests/Scenarios/Api/AspirationalExamples.cs).
+
+### Installation
+
+ML.NET supports Windows, MacOS, and Linux. See [supported OS versions of .NET
+Core
+2.0](https://github.com/dotnet/core/blob/master/release-notes/2.0/2.0-supported-os.md)
+for more details.
+
+You can install ML.NET NuGet from the CLI using:
+```
+dotnet add package Microsoft.ML
+```
+
+From package manager:
+```
+Install-Package Microsoft.ML
+```
+
+### Release Notes
+
+Below are some of the highlights from this release.
+
+* Added a TensorFlow model scoring transform (TensorFlowTransform)
+  ([#704](https://github.com/dotnet/machinelearning/pull/704))
+
+    * [TensorFlow](https://www.tensorflow.org/) is a popular machine learning
+      toolkit that enables training deep neural networks (and general numeric
+      computations).
+    * This transform enables taking an existing TensorFlow model, either
+      trained by you or downloaded from somewhere else, and get the scores
+      from the model in ML.NET.
+    * For now, these scores can be used within a `LearningPipeline` as inputs
+      to a learner. However, with the upcoming ML.NET APIs, the scores from
+      the TensorFlow model will be directly accessible.
+    * The implementation of this transform is based on code from
+      [TensorFlowSharp](https://github.com/migueldeicaza/TensorFlowSharp).
+    * Example usage of the transform with the existing `LearningPipeline` API
+      can be found
+      [here](https://github.com/dotnet/machinelearning/blob/6ac380a4d3f44ee7b015461f74c4298b0ed5184b/test/Microsoft.ML.Tests/Scenarios/TensorflowTests.cs)
+    * In the future, we will add functionality in ML.NET to enable identifying
+      the expected inputs and outputs of TensorFlow models. For now, the
+      TensorFlow APIs or a tool like
+      [Netron](https://github.com/lutzroeder/Netron) can be used.
+
+Additional issues closed in this milestone can be found
+[here](https://github.com/dotnet/machinelearning/milestone/4?closed=1).
+
+### Acknowledgements
+
+Shoutout to [adamsitnik](https://github.com/adamsitnik),
+[Jongkeun](https://github.com/Jongkeun), and the ML.NET team for their
+contributions as part of this release!