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# ML.NET 0.2 Release Notes

We would like to thank the community for the engagement so far and helping us
shape ML.NET.

Today we are releasing ML.NET 0.2. This release focuses on addressing
questions/issues, adding clustering to the list of supported machine learning
tasks, enabling using data from memory to train models, easier model
validation, and more.

### 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 clustering to the list of supported machine learning tasks

* Clustering is an unsupervised learning task that groups sets of items
based on their features. It identifies which items are more similar to
each other than other items. This might be useful in scenarios such as
organizing news articles into groups based on their topics, segmenting
users based on their shopping habits, and grouping viewers based on
their taste in movies.

* ML.NET 0.2 exposes `KMeansPlusPlusClusterer` which implements [K-Means++
clustering](http://theory.stanford.edu/~sergei/papers/vldb12-kmpar.pdf)
with [Yinyang K-means
acceleration](https://www.microsoft.com/en-us/research/publication/yinyang-k-means-a-drop-in-replacement-of-the-classic-k-means-with-consistent-speedup/?from=http%3A%2F%2Fresearch.microsoft.com%2Fapps%2Fpubs%2Fdefault.aspx%3Fid%3D252149).
[This
test](https://github.com/dotnet/machinelearning/blob/78810563616f3fcb0b63eb8a50b8b2e62d9d65fc/test/Microsoft.ML.Tests/Scenarios/ClusteringTests.cs)
shows how to use it (from
[#222](https://github.com/dotnet/machinelearning/pull/222)).

* Train using data objects in addition to loading data from a file using
`CollectionDataSource`. ML.NET 0.1 enabled loading data from a delimited
text file. `CollectionDataSource` in ML.NET 0.2 adds the ability to use a
collection of objects as the input to a `LearningPipeline`. See sample usage
[here](https://github.com/dotnet/machinelearning/blob/78810563616f3fcb0b63eb8a50b8b2e62d9d65fc/test/Microsoft.ML.Tests/CollectionDataSourceTests.cs#L133)
(from [#106](https://github.com/dotnet/machinelearning/pull/106)).

* Easier model validation with cross-validation and train-test

* [Cross-validation](https://en.wikipedia.org/wiki/Cross-validation_(statistics))
is an approach to validating how well your model statistically performs.
It does not require a separate test dataset, but rather uses your
training data to test your model (it partitions the data so different
data is used for training and testing, and it does this multiple times).
[Here](https://github.com/dotnet/machinelearning/blob/78810563616f3fcb0b63eb8a50b8b2e62d9d65fc/test/Microsoft.ML.Tests/Scenarios/SentimentPredictionTests.cs#L51)
is an example for doing cross-validation (from
[#212](https://github.com/dotnet/machinelearning/pull/212)).

* Train-test is a shortcut to testing your model on a separate dataset.
See example usage
[here](https://github.com/dotnet/machinelearning/blob/78810563616f3fcb0b63eb8a50b8b2e62d9d65fc/test/Microsoft.ML.Tests/Scenarios/SentimentPredictionTests.cs#L36).

* Note that the `LearningPipeline` is prepared the same way in both cases.

* Speed improvement for predictions: by not creating a parallel cursor for
dataviews that only have one element, we get a significant speed-up for
predictions (see
[#179](https://github.com/dotnet/machinelearning/issues/179) for a few
measurements).

* Updated `TextLoader` API: the `TextLoader` API is now code generated and was
updated to take explicit declarations for the columns in the data, which is
required in some scenarios. See
[#142](https://github.com/dotnet/machinelearning/pull/142).

* Added daily NuGet builds of the project: daily NuGet builds of ML.NET are
now available
[here](https://dotnet.myget.org/feed/dotnet-core/package/nuget/Microsoft.ML).

Additional issues closed in this milestone can be found [here](https://github.com/dotnet/machinelearning/milestone/1?closed=1).

### Acknowledgements

Shoutout to tincann, rantri, yamachu, pkulikov, Sorrien, v-tsymbalistyi, Ky7m,
forki, jessebenson, mfaticaearnin, and the ML.NET team for their contributions
as part of this release!
, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
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})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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# ML.NET 0.2 Release Notes

We would like to thank the community for the engagement so far and helping us
shape ML.NET.

Today we are releasing ML.NET 0.2. This release focuses on addressing
questions/issues, adding clustering to the list of supported machine learning
tasks, enabling using data from memory to train models, easier model
validation, and more.

### 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 clustering to the list of supported machine learning tasks

 * Clustering is an unsupervised learning task that groups sets of items
 based on their features. It identifies which items are more similar to
 each other than other items. This might be useful in scenarios such as
 organizing news articles into groups based on their topics, segmenting
 users based on their shopping habits, and grouping viewers based on
 their taste in movies. 

 * ML.NET 0.2 exposes `KMeansPlusPlusClusterer` which implements [K-Means++
 clustering](http://theory.stanford.edu/~sergei/papers/vldb12-kmpar.pdf)
 with [Yinyang K-means
 acceleration](https://www.microsoft.com/en-us/research/publication/yinyang-k-means-a-drop-in-replacement-of-the-classic-k-means-with-consistent-speedup/?from=http%3A%2F%2Fresearch.microsoft.com%2Fapps%2Fpubs%2Fdefault.aspx%3Fid%3D252149).
 [This
 test](https://github.com/dotnet/machinelearning/blob/78810563616f3fcb0b63eb8a50b8b2e62d9d65fc/test/Microsoft.ML.Tests/Scenarios/ClusteringTests.cs)
 shows how to use it (from
 [#222](https://github.com/dotnet/machinelearning/pull/222)).

* Train using data objects in addition to loading data from a file using
 `CollectionDataSource`. ML.NET 0.1 enabled loading data from a delimited
 text file. `CollectionDataSource` in ML.NET 0.2 adds the ability to use a
 collection of objects as the input to a `LearningPipeline`. See sample usage
 [here](https://github.com/dotnet/machinelearning/blob/78810563616f3fcb0b63eb8a50b8b2e62d9d65fc/test/Microsoft.ML.Tests/CollectionDataSourceTests.cs#L133)
 (from [#106](https://github.com/dotnet/machinelearning/pull/106)). 

* Easier model validation with cross-validation and train-test

 * [Cross-validation](https://en.wikipedia.org/wiki/Cross-validation_(statistics))
 is an approach to validating how well your model statistically performs.
 It does not require a separate test dataset, but rather uses your
 training data to test your model (it partitions the data so different
 data is used for training and testing, and it does this multiple times).
 [Here](https://github.com/dotnet/machinelearning/blob/78810563616f3fcb0b63eb8a50b8b2e62d9d65fc/test/Microsoft.ML.Tests/Scenarios/SentimentPredictionTests.cs#L51)
 is an example for doing cross-validation (from
 [#212](https://github.com/dotnet/machinelearning/pull/212)).

 * Train-test is a shortcut to testing your model on a separate dataset.
 See example usage
 [here](https://github.com/dotnet/machinelearning/blob/78810563616f3fcb0b63eb8a50b8b2e62d9d65fc/test/Microsoft.ML.Tests/Scenarios/SentimentPredictionTests.cs#L36).

 * Note that the `LearningPipeline` is prepared the same way in both cases.

* Speed improvement for predictions: by not creating a parallel cursor for
 dataviews that only have one element, we get a significant speed-up for
 predictions (see
 [#179](https://github.com/dotnet/machinelearning/issues/179) for a few
 measurements).

* Updated `TextLoader` API: the `TextLoader` API is now code generated and was
 updated to take explicit declarations for the columns in the data, which is
 required in some scenarios. See
 [#142](https://github.com/dotnet/machinelearning/pull/142).

* Added daily NuGet builds of the project: daily NuGet builds of ML.NET are
 now available
 [here](https://dotnet.myget.org/feed/dotnet-core/package/nuget/Microsoft.ML).

Additional issues closed in this milestone can be found [here](https://github.com/dotnet/machinelearning/milestone/1?closed=1).

### Acknowledgements

Shoutout to tincann, rantri, yamachu, pkulikov, Sorrien, v-tsymbalistyi, Ky7m,
forki, jessebenson, mfaticaearnin, and the ML.NET team for their contributions
as part of this release! 

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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 Merged

glebuk
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Jun 5, 2018
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Add release notes for ML.NET 0.2

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Jun 5, 2018
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# ML.NET 0.2 Release Notes

We would like to thank the community for the engagement so far and helping us
shape ML.NET.

Today we are releasing ML.NET 0.2. This release focuses on addressing
questions/issues, adding clustering to the list of supported machine learning
tasks, enabling using data from memory to train models, easier model
validation, and more.

### 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 clustering to the list of supported machine learning tasks

 * Clustering is an unsupervised learning task that groups sets of items
 based on their features. It identifies which items are more similar to
 each other than other items. This might be useful in scenarios such as
 organizing news articles into groups based on their topics, segmenting
 users based on their shopping habits, and grouping viewers based on
 their taste in movies. 

 * ML.NET 0.2 exposes `KMeansPlusPlusClusterer` which implements [K-Means++
 clustering](http://theory.stanford.edu/~sergei/papers/vldb12-kmpar.pdf)
 with [Yinyang K-means
 acceleration](https://www.microsoft.com/en-us/research/publication/yinyang-k-means-a-drop-in-replacement-of-the-classic-k-means-with-consistent-speedup/?from=http%3A%2F%2Fresearch.microsoft.com%2Fapps%2Fpubs%2Fdefault.aspx%3Fid%3D252149).
 [This
 test](https://github.com/dotnet/machinelearning/blob/78810563616f3fcb0b63eb8a50b8b2e62d9d65fc/test/Microsoft.ML.Tests/Scenarios/ClusteringTests.cs)
 shows how to use it (from
 [#222](https://github.com/dotnet/machinelearning/pull/222)).

* Train using data objects in addition to loading data from a file using
 `CollectionDataSource`. ML.NET 0.1 enabled loading data from a delimited
 text file. `CollectionDataSource` in ML.NET 0.2 adds the ability to use a
 collection of objects as the input to a `LearningPipeline`. See sample usage
 [here](https://github.com/dotnet/machinelearning/blob/78810563616f3fcb0b63eb8a50b8b2e62d9d65fc/test/Microsoft.ML.Tests/CollectionDataSourceTests.cs#L133)
 (from [#106](https://github.com/dotnet/machinelearning/pull/106)). 

* Easier model validation with cross-validation and train-test

 * [Cross-validation](https://en.wikipedia.org/wiki/Cross-validation_(statistics))
 is an approach to validating how well your model statistically performs.
 It does not require a separate test dataset, but rather uses your
 training data to test your model (it partitions the data so different
 data is used for training and testing, and it does this multiple times).
 [Here](https://github.com/dotnet/machinelearning/blob/78810563616f3fcb0b63eb8a50b8b2e62d9d65fc/test/Microsoft.ML.Tests/Scenarios/SentimentPredictionTests.cs#L51)
 is an example for doing cross-validation (from
 [#212](https://github.com/dotnet/machinelearning/pull/212)).

 * Train-test is a shortcut to testing your model on a separate dataset.
 See example usage
 [here](https://github.com/dotnet/machinelearning/blob/78810563616f3fcb0b63eb8a50b8b2e62d9d65fc/test/Microsoft.ML.Tests/Scenarios/SentimentPredictionTests.cs#L36).

 * Note that the `LearningPipeline` is prepared the same way in both cases.

* Speed improvement for predictions: by not creating a parallel cursor for
 dataviews that only have one element, we get a significant speed-up for
 predictions (see
 [#179](https://github.com/dotnet/machinelearning/issues/179) for a few
 measurements).

* Updated `TextLoader` API: the `TextLoader` API is now code generated and was
 updated to take explicit declarations for the columns in the data, which is
 required in some scenarios. See
 [#142](https://github.com/dotnet/machinelearning/pull/142).

* Added daily NuGet builds of the project: daily NuGet builds of ML.NET are
 now available
 [here](https://dotnet.myget.org/feed/dotnet-core/package/nuget/Microsoft.ML).

Additional issues closed in this milestone can be found [here](https://github.com/dotnet/machinelearning/milestone/1?closed=1).

### Acknowledgements

Shoutout to tincann, rantri, yamachu, pkulikov, Sorrien, v-tsymbalistyi, Ky7m,
forki, jessebenson, mfaticaearnin, and the ML.NET team for their contributions
as part of this release! 

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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glebuk
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Jun 5, 2018
 Merged


Add release notes for ML.NET 0.2
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4f241e9

Add release notes for ML.NET 0.2

Jun 5, 2018
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Adding release note about TextLoader changes and additional issue/PR …

Jun 5, 2018
f026db2

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Jun 5, 2018

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# ML.NET 0.2 Release Notes

We would like to thank the community for the engagement so far and helping us
shape ML.NET.

Today we are releasing ML.NET 0.2. This release focuses on addressing
questions/issues, adding clustering to the list of supported machine learning
tasks, enabling using data from memory to train models, easier model
validation, and more.

### 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 clustering to the list of supported machine learning tasks

 * Clustering is an unsupervised learning task that groups sets of items
 based on their features. It identifies which items are more similar to
 each other than other items. This might be useful in scenarios such as
 organizing news articles into groups based on their topics, segmenting
 users based on their shopping habits, and grouping viewers based on
 their taste in movies. 

 * ML.NET 0.2 exposes `KMeansPlusPlusClusterer` which implements [K-Means++
 clustering](http://theory.stanford.edu/~sergei/papers/vldb12-kmpar.pdf)
 with [Yinyang K-means
 acceleration](https://www.microsoft.com/en-us/research/publication/yinyang-k-means-a-drop-in-replacement-of-the-classic-k-means-with-consistent-speedup/?from=http%3A%2F%2Fresearch.microsoft.com%2Fapps%2Fpubs%2Fdefault.aspx%3Fid%3D252149).
 [This
 test](https://github.com/dotnet/machinelearning/blob/78810563616f3fcb0b63eb8a50b8b2e62d9d65fc/test/Microsoft.ML.Tests/Scenarios/ClusteringTests.cs)
 shows how to use it (from
 [#222](https://github.com/dotnet/machinelearning/pull/222)).

* Train using data objects in addition to loading data from a file using
 `CollectionDataSource`. ML.NET 0.1 enabled loading data from a delimited
 text file. `CollectionDataSource` in ML.NET 0.2 adds the ability to use a
 collection of objects as the input to a `LearningPipeline`. See sample usage
 [here](https://github.com/dotnet/machinelearning/blob/78810563616f3fcb0b63eb8a50b8b2e62d9d65fc/test/Microsoft.ML.Tests/CollectionDataSourceTests.cs#L133)
 (from [#106](https://github.com/dotnet/machinelearning/pull/106)). 

* Easier model validation with cross-validation and train-test

 * [Cross-validation](https://en.wikipedia.org/wiki/Cross-validation_(statistics))
 is an approach to validating how well your model statistically performs.
 It does not require a separate test dataset, but rather uses your
 training data to test your model (it partitions the data so different
 data is used for training and testing, and it does this multiple times).
 [Here](https://github.com/dotnet/machinelearning/blob/78810563616f3fcb0b63eb8a50b8b2e62d9d65fc/test/Microsoft.ML.Tests/Scenarios/SentimentPredictionTests.cs#L51)
 is an example for doing cross-validation (from
 [#212](https://github.com/dotnet/machinelearning/pull/212)).

 * Train-test is a shortcut to testing your model on a separate dataset.
 See example usage
 [here](https://github.com/dotnet/machinelearning/blob/78810563616f3fcb0b63eb8a50b8b2e62d9d65fc/test/Microsoft.ML.Tests/Scenarios/SentimentPredictionTests.cs#L36).

 * Note that the `LearningPipeline` is prepared the same way in both cases.

* Speed improvement for predictions: by not creating a parallel cursor for
 dataviews that only have one element, we get a significant speed-up for
 predictions (see
 [#179](https://github.com/dotnet/machinelearning/issues/179) for a few
 measurements).

* Updated `TextLoader` API: the `TextLoader` API is now code generated and was
 updated to take explicit declarations for the columns in the data, which is
 required in some scenarios. See
 [#142](https://github.com/dotnet/machinelearning/pull/142).

* Added daily NuGet builds of the project: daily NuGet builds of ML.NET are
 now available
 [here](https://dotnet.myget.org/feed/dotnet-core/package/nuget/Microsoft.ML).

Additional issues closed in this milestone can be found [here](https://github.com/dotnet/machinelearning/milestone/1?closed=1).

### Acknowledgements

Shoutout to tincann, rantri, yamachu, pkulikov, Sorrien, v-tsymbalistyi, Ky7m,
forki, jessebenson, mfaticaearnin, and the ML.NET team for their contributions
as part of this release! 

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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# ML.NET 0.2 Release Notes

We would like to thank the community for the engagement so far and helping us
shape ML.NET.

Today we are releasing ML.NET 0.2. This release focuses on addressing
questions/issues, adding clustering to the list of supported machine learning
tasks, enabling using data from memory to train models, easier model
validation, and more.

### 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 clustering to the list of supported machine learning tasks

 * Clustering is an unsupervised learning task that groups sets of items
 based on their features. It identifies which items are more similar to
 each other than other items. This might be useful in scenarios such as
 organizing news articles into groups based on their topics, segmenting
 users based on their shopping habits, and grouping viewers based on
 their taste in movies. 

 * ML.NET 0.2 exposes `KMeansPlusPlusClusterer` which implements [K-Means++
 clustering](http://theory.stanford.edu/~sergei/papers/vldb12-kmpar.pdf)
 with [Yinyang K-means
 acceleration](https://www.microsoft.com/en-us/research/publication/yinyang-k-means-a-drop-in-replacement-of-the-classic-k-means-with-consistent-speedup/?from=http%3A%2F%2Fresearch.microsoft.com%2Fapps%2Fpubs%2Fdefault.aspx%3Fid%3D252149).
 [This
 test](https://github.com/dotnet/machinelearning/blob/78810563616f3fcb0b63eb8a50b8b2e62d9d65fc/test/Microsoft.ML.Tests/Scenarios/ClusteringTests.cs)
 shows how to use it (from
 [#222](https://github.com/dotnet/machinelearning/pull/222)).

* Train using data objects in addition to loading data from a file using
 `CollectionDataSource`. ML.NET 0.1 enabled loading data from a delimited
 text file. `CollectionDataSource` in ML.NET 0.2 adds the ability to use a
 collection of objects as the input to a `LearningPipeline`. See sample usage
 [here](https://github.com/dotnet/machinelearning/blob/78810563616f3fcb0b63eb8a50b8b2e62d9d65fc/test/Microsoft.ML.Tests/CollectionDataSourceTests.cs#L133)
 (from [#106](https://github.com/dotnet/machinelearning/pull/106)). 

* Easier model validation with cross-validation and train-test

 * [Cross-validation](https://en.wikipedia.org/wiki/Cross-validation_(statistics))
 is an approach to validating how well your model statistically performs.
 It does not require a separate test dataset, but rather uses your
 training data to test your model (it partitions the data so different
 data is used for training and testing, and it does this multiple times).
 [Here](https://github.com/dotnet/machinelearning/blob/78810563616f3fcb0b63eb8a50b8b2e62d9d65fc/test/Microsoft.ML.Tests/Scenarios/SentimentPredictionTests.cs#L51)
 is an example for doing cross-validation (from
 [#212](https://github.com/dotnet/machinelearning/pull/212)).

 * Train-test is a shortcut to testing your model on a separate dataset.
 See example usage
 [here](https://github.com/dotnet/machinelearning/blob/78810563616f3fcb0b63eb8a50b8b2e62d9d65fc/test/Microsoft.ML.Tests/Scenarios/SentimentPredictionTests.cs#L36).

 * Note that the `LearningPipeline` is prepared the same way in both cases.

* Speed improvement for predictions: by not creating a parallel cursor for
 dataviews that only have one element, we get a significant speed-up for
 predictions (see
 [#179](https://github.com/dotnet/machinelearning/issues/179) for a few
 measurements).

* Updated `TextLoader` API: the `TextLoader` API is now code generated and was
 updated to take explicit declarations for the columns in the data, which is
 required in some scenarios. See
 [#142](https://github.com/dotnet/machinelearning/pull/142).

* Added daily NuGet builds of the project: daily NuGet builds of ML.NET are
 now available
 [here](https://dotnet.myget.org/feed/dotnet-core/package/nuget/Microsoft.ML).

Additional issues closed in this milestone can be found [here](https://github.com/dotnet/machinelearning/milestone/1?closed=1).

### Acknowledgements

Shoutout to tincann, rantri, yamachu, pkulikov, Sorrien, v-tsymbalistyi, Ky7m,
forki, jessebenson, mfaticaearnin, and the ML.NET team for their contributions
as part of this release! 

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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docs/release-notes/0.2/release-0.2.md

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Original file line numberDiff line numberDiff line change
@@ -0,0 +1,95 @@
# ML.NET 0.2 Release Notes

We would like to thank the community for the engagement so far and helping us
shape ML.NET.

Today we are releasing ML.NET 0.2. This release focuses on addressing
questions/issues, adding clustering to the list of supported machine learning
tasks, enabling using data from memory to train models, easier model
validation, and more.

### 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 clustering to the list of supported machine learning tasks

 * Clustering is an unsupervised learning task that groups sets of items
 based on their features. It identifies which items are more similar to
 each other than other items. This might be useful in scenarios such as
 organizing news articles into groups based on their topics, segmenting
 users based on their shopping habits, and grouping viewers based on
 their taste in movies. 

 * ML.NET 0.2 exposes `KMeansPlusPlusClusterer` which implements [K-Means++
 clustering](http://theory.stanford.edu/~sergei/papers/vldb12-kmpar.pdf)
 with [Yinyang K-means
 acceleration](https://www.microsoft.com/en-us/research/publication/yinyang-k-means-a-drop-in-replacement-of-the-classic-k-means-with-consistent-speedup/?from=http%3A%2F%2Fresearch.microsoft.com%2Fapps%2Fpubs%2Fdefault.aspx%3Fid%3D252149).
 [This
 test](https://github.com/dotnet/machinelearning/blob/78810563616f3fcb0b63eb8a50b8b2e62d9d65fc/test/Microsoft.ML.Tests/Scenarios/ClusteringTests.cs)
 shows how to use it (from
 [#222](https://github.com/dotnet/machinelearning/pull/222)).

* Train using data objects in addition to loading data from a file using
 `CollectionDataSource`. ML.NET 0.1 enabled loading data from a delimited
 text file. `CollectionDataSource` in ML.NET 0.2 adds the ability to use a
 collection of objects as the input to a `LearningPipeline`. See sample usage
 [here](https://github.com/dotnet/machinelearning/blob/78810563616f3fcb0b63eb8a50b8b2e62d9d65fc/test/Microsoft.ML.Tests/CollectionDataSourceTests.cs#L133)
 (from [#106](https://github.com/dotnet/machinelearning/pull/106)). 

* Easier model validation with cross-validation and train-test

 * [Cross-validation](https://en.wikipedia.org/wiki/Cross-validation_(statistics))
 is an approach to validating how well your model statistically performs.
 It does not require a separate test dataset, but rather uses your
 training data to test your model (it partitions the data so different
 data is used for training and testing, and it does this multiple times).
 [Here](https://github.com/dotnet/machinelearning/blob/78810563616f3fcb0b63eb8a50b8b2e62d9d65fc/test/Microsoft.ML.Tests/Scenarios/SentimentPredictionTests.cs#L51)
 is an example for doing cross-validation (from
 [#212](https://github.com/dotnet/machinelearning/pull/212)).

 * Train-test is a shortcut to testing your model on a separate dataset.
 See example usage
 [here](https://github.com/dotnet/machinelearning/blob/78810563616f3fcb0b63eb8a50b8b2e62d9d65fc/test/Microsoft.ML.Tests/Scenarios/SentimentPredictionTests.cs#L36).

 * Note that the `LearningPipeline` is prepared the same way in both cases.

* Speed improvement for predictions: by not creating a parallel cursor for
 dataviews that only have one element, we get a significant speed-up for
 predictions (see
 [#179](https://github.com/dotnet/machinelearning/issues/179) for a few
 measurements).

* Updated `TextLoader` API: the `TextLoader` API is now code generated and was
 updated to take explicit declarations for the columns in the data, which is
 required in some scenarios. See
 [#142](https://github.com/dotnet/machinelearning/pull/142).

* Added daily NuGet builds of the project: daily NuGet builds of ML.NET are
 now available
 [here](https://dotnet.myget.org/feed/dotnet-core/package/nuget/Microsoft.ML).

Additional issues closed in this milestone can be found [here](https://github.com/dotnet/machinelearning/milestone/1?closed=1).

### Acknowledgements

Shoutout to tincann, rantri, yamachu, pkulikov, Sorrien, v-tsymbalistyi, Ky7m,
forki, jessebenson, mfaticaearnin, and the ML.NET team for their contributions
as part of this release! 

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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glebuk
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#301
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4f241e9

Add release notes for ML.NET 0.2

Jun 5, 2018
87b20ce

Adding release note about TextLoader changes and additional issue/PR …

Jun 5, 2018
f026db2

Addressing comments: fixing typos, changing formatting, and adding re…

Jun 5, 2018

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docs/release-notes/0.2/release-0.2.md

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Original file line numberDiff line numberDiff line change
@@ -0,0 +1,95 @@
# ML.NET 0.2 Release Notes

We would like to thank the community for the engagement so far and helping us
shape ML.NET.

Today we are releasing ML.NET 0.2. This release focuses on addressing
questions/issues, adding clustering to the list of supported machine learning
tasks, enabling using data from memory to train models, easier model
validation, and more.

### 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 clustering to the list of supported machine learning tasks

 * Clustering is an unsupervised learning task that groups sets of items
 based on their features. It identifies which items are more similar to
 each other than other items. This might be useful in scenarios such as
 organizing news articles into groups based on their topics, segmenting
 users based on their shopping habits, and grouping viewers based on
 their taste in movies. 

 * ML.NET 0.2 exposes `KMeansPlusPlusClusterer` which implements [K-Means++
 clustering](http://theory.stanford.edu/~sergei/papers/vldb12-kmpar.pdf)
 with [Yinyang K-means
 acceleration](https://www.microsoft.com/en-us/research/publication/yinyang-k-means-a-drop-in-replacement-of-the-classic-k-means-with-consistent-speedup/?from=http%3A%2F%2Fresearch.microsoft.com%2Fapps%2Fpubs%2Fdefault.aspx%3Fid%3D252149).
 [This
 test](https://github.com/dotnet/machinelearning/blob/78810563616f3fcb0b63eb8a50b8b2e62d9d65fc/test/Microsoft.ML.Tests/Scenarios/ClusteringTests.cs)
 shows how to use it (from
 [#222](https://github.com/dotnet/machinelearning/pull/222)).

* Train using data objects in addition to loading data from a file using
 `CollectionDataSource`. ML.NET 0.1 enabled loading data from a delimited
 text file. `CollectionDataSource` in ML.NET 0.2 adds the ability to use a
 collection of objects as the input to a `LearningPipeline`. See sample usage
 [here](https://github.com/dotnet/machinelearning/blob/78810563616f3fcb0b63eb8a50b8b2e62d9d65fc/test/Microsoft.ML.Tests/CollectionDataSourceTests.cs#L133)
 (from [#106](https://github.com/dotnet/machinelearning/pull/106)). 

* Easier model validation with cross-validation and train-test

 * [Cross-validation](https://en.wikipedia.org/wiki/Cross-validation_(statistics))
 is an approach to validating how well your model statistically performs.
 It does not require a separate test dataset, but rather uses your
 training data to test your model (it partitions the data so different
 data is used for training and testing, and it does this multiple times).
 [Here](https://github.com/dotnet/machinelearning/blob/78810563616f3fcb0b63eb8a50b8b2e62d9d65fc/test/Microsoft.ML.Tests/Scenarios/SentimentPredictionTests.cs#L51)
 is an example for doing cross-validation (from
 [#212](https://github.com/dotnet/machinelearning/pull/212)).

 * Train-test is a shortcut to testing your model on a separate dataset.
 See example usage
 [here](https://github.com/dotnet/machinelearning/blob/78810563616f3fcb0b63eb8a50b8b2e62d9d65fc/test/Microsoft.ML.Tests/Scenarios/SentimentPredictionTests.cs#L36).

 * Note that the `LearningPipeline` is prepared the same way in both cases.

* Speed improvement for predictions: by not creating a parallel cursor for
 dataviews that only have one element, we get a significant speed-up for
 predictions (see
 [#179](https://github.com/dotnet/machinelearning/issues/179) for a few
 measurements).

* Updated `TextLoader` API: the `TextLoader` API is now code generated and was
 updated to take explicit declarations for the columns in the data, which is
 required in some scenarios. See
 [#142](https://github.com/dotnet/machinelearning/pull/142).

* Added daily NuGet builds of the project: daily NuGet builds of ML.NET are
 now available
 [here](https://dotnet.myget.org/feed/dotnet-core/package/nuget/Microsoft.ML).

Additional issues closed in this milestone can be found [here](https://github.com/dotnet/machinelearning/milestone/1?closed=1).

### Acknowledgements

Shoutout to tincann, rantri, yamachu, pkulikov, Sorrien, v-tsymbalistyi, Ky7m,
forki, jessebenson, mfaticaearnin, and the ML.NET team for their contributions
as part of this release! 

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

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 Merged

glebuk
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Jun 5, 2018
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Add release notes for ML.NET 0.2
#301
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4f241e9

Add release notes for ML.NET 0.2

Jun 5, 2018
87b20ce

Adding release note about TextLoader changes and additional issue/PR …

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# ML.NET 0.2 Release Notes

We would like to thank the community for the engagement so far and helping us
shape ML.NET.

Today we are releasing ML.NET 0.2. This release focuses on addressing
questions/issues, adding clustering to the list of supported machine learning
tasks, enabling using data from memory to train models, easier model
validation, and more.

### 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 clustering to the list of supported machine learning tasks

 * Clustering is an unsupervised learning task that groups sets of items
 based on their features. It identifies which items are more similar to
 each other than other items. This might be useful in scenarios such as
 organizing news articles into groups based on their topics, segmenting
 users based on their shopping habits, and grouping viewers based on
 their taste in movies. 

 * ML.NET 0.2 exposes `KMeansPlusPlusClusterer` which implements [K-Means++
 clustering](http://theory.stanford.edu/~sergei/papers/vldb12-kmpar.pdf)
 with [Yinyang K-means
 acceleration](https://www.microsoft.com/en-us/research/publication/yinyang-k-means-a-drop-in-replacement-of-the-classic-k-means-with-consistent-speedup/?from=http%3A%2F%2Fresearch.microsoft.com%2Fapps%2Fpubs%2Fdefault.aspx%3Fid%3D252149).
 [This
 test](https://github.com/dotnet/machinelearning/blob/78810563616f3fcb0b63eb8a50b8b2e62d9d65fc/test/Microsoft.ML.Tests/Scenarios/ClusteringTests.cs)
 shows how to use it (from
 [#222](https://github.com/dotnet/machinelearning/pull/222)).

* Train using data objects in addition to loading data from a file using
 `CollectionDataSource`. ML.NET 0.1 enabled loading data from a delimited
 text file. `CollectionDataSource` in ML.NET 0.2 adds the ability to use a
 collection of objects as the input to a `LearningPipeline`. See sample usage
 [here](https://github.com/dotnet/machinelearning/blob/78810563616f3fcb0b63eb8a50b8b2e62d9d65fc/test/Microsoft.ML.Tests/CollectionDataSourceTests.cs#L133)
 (from [#106](https://github.com/dotnet/machinelearning/pull/106)). 

* Easier model validation with cross-validation and train-test

 * [Cross-validation](https://en.wikipedia.org/wiki/Cross-validation_(statistics))
 is an approach to validating how well your model statistically performs.
 It does not require a separate test dataset, but rather uses your
 training data to test your model (it partitions the data so different
 data is used for training and testing, and it does this multiple times).
 [Here](https://github.com/dotnet/machinelearning/blob/78810563616f3fcb0b63eb8a50b8b2e62d9d65fc/test/Microsoft.ML.Tests/Scenarios/SentimentPredictionTests.cs#L51)
 is an example for doing cross-validation (from
 [#212](https://github.com/dotnet/machinelearning/pull/212)).

 * Train-test is a shortcut to testing your model on a separate dataset.
 See example usage
 [here](https://github.com/dotnet/machinelearning/blob/78810563616f3fcb0b63eb8a50b8b2e62d9d65fc/test/Microsoft.ML.Tests/Scenarios/SentimentPredictionTests.cs#L36).

 * Note that the `LearningPipeline` is prepared the same way in both cases.

* Speed improvement for predictions: by not creating a parallel cursor for
 dataviews that only have one element, we get a significant speed-up for
 predictions (see
 [#179](https://github.com/dotnet/machinelearning/issues/179) for a few
 measurements).

* Updated `TextLoader` API: the `TextLoader` API is now code generated and was
 updated to take explicit declarations for the columns in the data, which is
 required in some scenarios. See
 [#142](https://github.com/dotnet/machinelearning/pull/142).

* Added daily NuGet builds of the project: daily NuGet builds of ML.NET are
 now available
 [here](https://dotnet.myget.org/feed/dotnet-core/package/nuget/Microsoft.ML).

Additional issues closed in this milestone can be found [here](https://github.com/dotnet/machinelearning/milestone/1?closed=1).

### Acknowledgements

Shoutout to tincann, rantri, yamachu, pkulikov, Sorrien, v-tsymbalistyi, Ky7m,
forki, jessebenson, mfaticaearnin, and the ML.NET team for their contributions
as part of this release!