Add release notes for ML.NET 0.3 (dotnet#476)

Author: GalOshri

docs/release-notes/0.3/release-0.3.md

@@ -0,0 +1,114 @@
+# ML.NET 0.3 Release Notes
+
+Today we are releasing ML.NET 0.3. This release focuses on adding components
+to ML.NET from the internal codebase (such as Factorization Machines,
+LightGBM, Ensembles, and LightLDA), enabling export to the ONNX model format,
+and bug fixes.
+
+### 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 Field-Aware Factorization Machines (FFM) as a learner for binary
+  classification (#383)
+
+    * FFM is useful for various large sparse datasets, especially in areas
+      such as recommendations and click prediction. It has been used to win
+      various click prediction competitions such as the [Criteo Display
+      Advertising Challenge on
+      Kaggle](https://www.kaggle.com/c/criteo-display-ad-challenge). You can
+      learn more about the winning solution
+      [here](https://www.csie.ntu.edu.tw/~r01922136/kaggle-2014-criteo.pdf).
+    * FFM is a streaming learner so it does not require the entire dataset to
+      fit in memory.
+    * You can learn more about FFM
+      [here](http://www.csie.ntu.edu.tw/~cjlin/papers/ffm.pdf) and some of the
+      speedup approaches that are used in ML.NET
+      [here](https://github.com/wschin/fast-ffm/blob/master/fast-ffm.pdf).
+
+* Added [LightGBM](https://github.com/Microsoft/LightGBM) as a learner for
+  binary classification, multiclass classification, and regression (#392)
+
+    * LightGBM is a tree based gradient boosting machine. It is under the
+      umbrella of the [DMTK](http://github.com/microsoft/dmtk) project at
+      Microsoft.
+    * The LightGBM repository shows various [comparison
+      experiments](https://github.com/Microsoft/LightGBM/blob/6488f319f243f7ff679a8e388a33e758c5802303/docs/Experiments.rst#comparison-experiment)
+      that show good accuracy and speed, so it is a great learner to try out.
+      It has also been used in winning solutions in various [ML
+      challenges](https://github.com/Microsoft/LightGBM/blob/a6e878e2fc6e7f545921cbe337cc511fbd1f500d/examples/README.md).
+    * This addition wraps LightGBM and exposes it in ML.NET.
+    * Note that LightGBM can also be used for ranking, but the ranking
+      evaluator is not yet exposed in ML.NET.
+
+* Added Ensemble learners for binary classification, multiclass
+  classification, and regression (#379)
+
+    * [Ensemble learners](https://en.wikipedia.org/wiki/Ensemble_learning)
+      enable using multiple learners in one model. As an example, the Ensemble
+      learner could train both `FastTree` and `AveragedPerceptron` and average
+      their predictions to get the final prediction. 
+    * Combining multiple models of similar statistical performance may lead to
+      better performance than each model separately.
+
+* Added LightLDA transform for topic modeling (#377)
+
+    * LightLDA is an implementation of [Latent Dirichlet
+      Allocation](https://en.wikipedia.org/wiki/Latent_Dirichlet_allocation)
+      which infers topical structure from text data. 
+    * The implementation of LightLDA in ML.NET is based on [this
+      paper](https://arxiv.org/abs/1412.1576). There is a distributed
+      implementation of LightLDA
+      [here](https://github.com/Microsoft/lightlda).
+
+* Added One-Versus-All (OVA) learner for multiclass classification (#363)
+
+    * [OVA](https://en.wikipedia.org/wiki/Multiclass_classification#One-vs.-rest)
+      (sometimes known as One-Versus-Rest) is an approach to using binary
+      classifiers in multiclass classification problems. 
+    * While some binary classification learners in ML.NET natively support
+      multiclass classification (e.g. Logistic Regression), there are others
+      that do not (e.g. Averaged Perceptron). OVA enables using the latter
+      group for multiclass classification as well.
+
+* Enabled export of ML.NET models to the [ONNX](https://onnx.ai/) format
+  (#248)
+
+    * ONNX is a common format for representing deep learning models (also
+      supporting certain other types of models) which enables developers to
+      move models between different ML toolkits.
+    * ONNX models can be used in [Windows
+      ML](https://docs.microsoft.com/en-us/windows/uwp/machine-learning/overview)
+      which enables evaluating models on Windows 10 devices and taking
+      advantage of capabilities like hardware acceleration.
+    * Currently, only a subset of ML.NET components can be used in a model
+      that is converted to ONNX. 
+
+Additional issues closed in this milestone can be found
+[here](https://github.com/dotnet/machinelearning/milestone/2?closed=1).
+
+### Acknowledgements
+
+Shoutout to [pkulikov](https://github.com/pkulikov),
+[veikkoeeva](https://github.com/veikkoeeva),
+[ross-p-smith](https://github.com/ross-p-smith),
+[jwood803](https://github.com/jwood803),
+[Nepomuceno](https://github.com/Nepomuceno), and the ML.NET team for their
+contributions as part of this release!