Add release notes for ML.NET 0.1#37

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Add release notes for ML.NET 0.1 #37

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Add release notes for ML.NET 0.1
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# ML.NET 0.1 Release Notes

ML.NET 0.1 is the first preview release of ML.NET. Thank you for trying it out and we look forward to your feedback! Try training, scoring, and using machine learning models in your app and tell us how it goes.

### Installation

ML.NET works on any platform that supports [.NET Core 2.0](https://www.microsoft.com/net/learn/get-started/windows). It also works on the .NET Framework.

You can install ML.NET NuGet from the .NET Core CLI using:
```
dotnet add package Microsoft.ML
```

From package manager:
```
Install-Package Microsoft.ML
```

Or from within Visual Studio's NuGet package manager.

### Release Notes

This initial release contains core ML.NET components for enabling machine learning pipelines:

* ML Data Structures (e.g. `IDataView`, `LearningPipeline`)

* TextLoader (loading data from a delimited text file into a `LearningPipeline`)

* Transforms (to get data in the correct format for training):
* Processing/featurizing text: `TextFeaturizer`
* Schema modifcation: `ColumnConcatenator`, `ColumnSelector`, and `ColumnDropper`
* Working with categorical features: `CategoricalOneHotVectorizer` and `CategoricalHashOneHotVectorizer`
* Dealing with missing data: `MissingValueHandler`
* Filters: `RowTakeFilter`, `RowSkipFilter`, `RowRangeFilter`
* Feature selection: `FeatureSelectorByCount` and `FeatureSelectorByMutualInformation`

* Learners (to train machine learning models) for a variety of tasks:
* Binary classification: `FastTreeBinaryClassifier`, `StochasticDualCoordinateAscentBinaryClassifier`, `AveragedPerceptronBinaryClassifier`, `BinaryLogisticRegressor`, `FastForestBinaryClassifier`, `LinearSvmBinaryClassifier`, and `GeneralizedAdditiveModelBinaryClassifier`
* Multiclass classification: `StochasticDualCoordinateAscentClassifier`, `LogisticRegressor`, and`NaiveBayesClassifier`
* Regression: `FastTreeRegressor`, `FastTreeTweedieRegressor`, `StochasticDualCoordinateAscentRegressor`, `OrdinaryLeastSquaresRegressor`, `OnlineGradientDescentRegressor`, `PoissonRegressor`, and `GeneralizedAdditiveModelRegressor`

* Evaluators (to check how well the model works):
* For Binary classification: `BinaryClassificationEvaluator`
* For Multiclass classification: `ClassificationEvaluator`
* For Regression: `RegressionEvaluator`

Additional components have been included in the repository but cannot be used in the `LearningPipeline` yet (this will be updated in future releases).
, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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Add release notes for ML.NET 0.1

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# ML.NET 0.1 Release Notes

ML.NET 0.1 is the first preview release of ML.NET. Thank you for trying it out and we look forward to your feedback! Try training, scoring, and using machine learning models in your app and tell us how it goes.

### Installation

ML.NET works on any platform that supports [.NET Core 2.0](https://www.microsoft.com/net/learn/get-started/windows). It also works on the .NET Framework.

You can install ML.NET NuGet from the .NET Core CLI using:
```
dotnet add package Microsoft.ML
```

From package manager:
```
Install-Package Microsoft.ML
```

Or from within Visual Studio's NuGet package manager.

### Release Notes

This initial release contains core ML.NET components for enabling machine learning pipelines:

* ML Data Structures (e.g. `IDataView`, `LearningPipeline`)

* TextLoader (loading data from a delimited text file into a `LearningPipeline`)

* Transforms (to get data in the correct format for training):
 * Processing/featurizing text: `TextFeaturizer`
 * Schema modifcation: `ColumnConcatenator`, `ColumnSelector`, and `ColumnDropper`
 * Working with categorical features: `CategoricalOneHotVectorizer` and `CategoricalHashOneHotVectorizer`
 * Dealing with missing data: `MissingValueHandler`
 * Filters: `RowTakeFilter`, `RowSkipFilter`, `RowRangeFilter`
 * Feature selection: `FeatureSelectorByCount` and `FeatureSelectorByMutualInformation`

* Learners (to train machine learning models) for a variety of tasks:
 * Binary classification: `FastTreeBinaryClassifier`, `StochasticDualCoordinateAscentBinaryClassifier`, `AveragedPerceptronBinaryClassifier`, `BinaryLogisticRegressor`, `FastForestBinaryClassifier`, `LinearSvmBinaryClassifier`, and `GeneralizedAdditiveModelBinaryClassifier`
 * Multiclass classification: `StochasticDualCoordinateAscentClassifier`, `LogisticRegressor`, and`NaiveBayesClassifier`
 * Regression: `FastTreeRegressor`, `FastTreeTweedieRegressor`, `StochasticDualCoordinateAscentRegressor`, `OrdinaryLeastSquaresRegressor`, `OnlineGradientDescentRegressor`, `PoissonRegressor`, and `GeneralizedAdditiveModelRegressor`

* Evaluators (to check how well the model works):
 * For Binary classification: `BinaryClassificationEvaluator`
 * For Multiclass classification: `ClassificationEvaluator`
 * For Regression: `RegressionEvaluator`

Additional components have been included in the repository but cannot be used in the `LearningPipeline` yet (this will be updated in future releases).

, '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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Add release notes for ML.NET 0.1#37

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 Merged

shauheen
merged 3 commits into

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May 7, 2018
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Add release notes for ML.NET 0.1
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6c93efe

Add release notes for ML.NET 0.1

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d4688ad

Merge remote-tracking branch 'upstream/master'

May 6, 2018
3530b52

Release note tweaks

May 7, 2018

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Original file line numberDiff line numberDiff line change
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# ML.NET 0.1 Release Notes

ML.NET 0.1 is the first preview release of ML.NET. Thank you for trying it out and we look forward to your feedback! Try training, scoring, and using machine learning models in your app and tell us how it goes.

### Installation

ML.NET works on any platform that supports [.NET Core 2.0](https://www.microsoft.com/net/learn/get-started/windows). It also works on the .NET Framework.

You can install ML.NET NuGet from the .NET Core CLI using:
```
dotnet add package Microsoft.ML
```

From package manager:
```
Install-Package Microsoft.ML
```

Or from within Visual Studio's NuGet package manager.

### Release Notes

This initial release contains core ML.NET components for enabling machine learning pipelines:

* ML Data Structures (e.g. `IDataView`, `LearningPipeline`)

* TextLoader (loading data from a delimited text file into a `LearningPipeline`)

* Transforms (to get data in the correct format for training):
 * Processing/featurizing text: `TextFeaturizer`
 * Schema modifcation: `ColumnConcatenator`, `ColumnSelector`, and `ColumnDropper`
 * Working with categorical features: `CategoricalOneHotVectorizer` and `CategoricalHashOneHotVectorizer`
 * Dealing with missing data: `MissingValueHandler`
 * Filters: `RowTakeFilter`, `RowSkipFilter`, `RowRangeFilter`
 * Feature selection: `FeatureSelectorByCount` and `FeatureSelectorByMutualInformation`

* Learners (to train machine learning models) for a variety of tasks:
 * Binary classification: `FastTreeBinaryClassifier`, `StochasticDualCoordinateAscentBinaryClassifier`, `AveragedPerceptronBinaryClassifier`, `BinaryLogisticRegressor`, `FastForestBinaryClassifier`, `LinearSvmBinaryClassifier`, and `GeneralizedAdditiveModelBinaryClassifier`
 * Multiclass classification: `StochasticDualCoordinateAscentClassifier`, `LogisticRegressor`, and`NaiveBayesClassifier`
 * Regression: `FastTreeRegressor`, `FastTreeTweedieRegressor`, `StochasticDualCoordinateAscentRegressor`, `OrdinaryLeastSquaresRegressor`, `OnlineGradientDescentRegressor`, `PoissonRegressor`, and `GeneralizedAdditiveModelRegressor`

* Evaluators (to check how well the model works):
 * For Binary classification: `BinaryClassificationEvaluator`
 * For Multiclass classification: `ClassificationEvaluator`
 * For Regression: `RegressionEvaluator`

Additional components have been included in the repository but cannot be used in the `LearningPipeline` yet (this will be updated in future releases).

, '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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 Merged

shauheen
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May 7, 2018
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Add release notes for ML.NET 0.1
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6c93efe

Add release notes for ML.NET 0.1

May 6, 2018
d4688ad

Merge remote-tracking branch 'upstream/master'

May 6, 2018
3530b52

Release note tweaks

May 7, 2018

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Documentation/release-notes/0.1/release-0.1.md

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Original file line numberDiff line numberDiff line change
@@ -0,0 +1,47 @@
# ML.NET 0.1 Release Notes

ML.NET 0.1 is the first preview release of ML.NET. Thank you for trying it out and we look forward to your feedback! Try training, scoring, and using machine learning models in your app and tell us how it goes.

### Installation

ML.NET works on any platform that supports [.NET Core 2.0](https://www.microsoft.com/net/learn/get-started/windows). It also works on the .NET Framework.

You can install ML.NET NuGet from the .NET Core CLI using:
```
dotnet add package Microsoft.ML
```

From package manager:
```
Install-Package Microsoft.ML
```

Or from within Visual Studio's NuGet package manager.

### Release Notes

This initial release contains core ML.NET components for enabling machine learning pipelines:

* ML Data Structures (e.g. `IDataView`, `LearningPipeline`)

* TextLoader (loading data from a delimited text file into a `LearningPipeline`)

* Transforms (to get data in the correct format for training):
 * Processing/featurizing text: `TextFeaturizer`
 * Schema modifcation: `ColumnConcatenator`, `ColumnSelector`, and `ColumnDropper`
 * Working with categorical features: `CategoricalOneHotVectorizer` and `CategoricalHashOneHotVectorizer`
 * Dealing with missing data: `MissingValueHandler`
 * Filters: `RowTakeFilter`, `RowSkipFilter`, `RowRangeFilter`
 * Feature selection: `FeatureSelectorByCount` and `FeatureSelectorByMutualInformation`

* Learners (to train machine learning models) for a variety of tasks:
 * Binary classification: `FastTreeBinaryClassifier`, `StochasticDualCoordinateAscentBinaryClassifier`, `AveragedPerceptronBinaryClassifier`, `BinaryLogisticRegressor`, `FastForestBinaryClassifier`, `LinearSvmBinaryClassifier`, and `GeneralizedAdditiveModelBinaryClassifier`
 * Multiclass classification: `StochasticDualCoordinateAscentClassifier`, `LogisticRegressor`, and`NaiveBayesClassifier`
 * Regression: `FastTreeRegressor`, `FastTreeTweedieRegressor`, `StochasticDualCoordinateAscentRegressor`, `OrdinaryLeastSquaresRegressor`, `OnlineGradientDescentRegressor`, `PoissonRegressor`, and `GeneralizedAdditiveModelRegressor`

* Evaluators (to check how well the model works):
 * For Binary classification: `BinaryClassificationEvaluator`
 * For Multiclass classification: `ClassificationEvaluator`
 * For Regression: `RegressionEvaluator`

Additional components have been included in the repository but cannot be used in the `LearningPipeline` yet (this will be updated in future releases).

, '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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 Merged

shauheen
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May 7, 2018
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Add release notes for ML.NET 0.1
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6c93efe

Add release notes for ML.NET 0.1

May 6, 2018
d4688ad

Merge remote-tracking branch 'upstream/master'

May 6, 2018
3530b52

Release note tweaks

May 7, 2018

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Documentation/release-notes/0.1/release-0.1.md

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Original file line numberDiff line numberDiff line change
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# ML.NET 0.1 Release Notes

ML.NET 0.1 is the first preview release of ML.NET. Thank you for trying it out and we look forward to your feedback! Try training, scoring, and using machine learning models in your app and tell us how it goes.

### Installation

ML.NET works on any platform that supports [.NET Core 2.0](https://www.microsoft.com/net/learn/get-started/windows). It also works on the .NET Framework.

You can install ML.NET NuGet from the .NET Core CLI using:
```
dotnet add package Microsoft.ML
```

From package manager:
```
Install-Package Microsoft.ML
```

Or from within Visual Studio's NuGet package manager.

### Release Notes

This initial release contains core ML.NET components for enabling machine learning pipelines:

* ML Data Structures (e.g. `IDataView`, `LearningPipeline`)

* TextLoader (loading data from a delimited text file into a `LearningPipeline`)

* Transforms (to get data in the correct format for training):
 * Processing/featurizing text: `TextFeaturizer`
 * Schema modifcation: `ColumnConcatenator`, `ColumnSelector`, and `ColumnDropper`
 * Working with categorical features: `CategoricalOneHotVectorizer` and `CategoricalHashOneHotVectorizer`
 * Dealing with missing data: `MissingValueHandler`
 * Filters: `RowTakeFilter`, `RowSkipFilter`, `RowRangeFilter`
 * Feature selection: `FeatureSelectorByCount` and `FeatureSelectorByMutualInformation`

* Learners (to train machine learning models) for a variety of tasks:
 * Binary classification: `FastTreeBinaryClassifier`, `StochasticDualCoordinateAscentBinaryClassifier`, `AveragedPerceptronBinaryClassifier`, `BinaryLogisticRegressor`, `FastForestBinaryClassifier`, `LinearSvmBinaryClassifier`, and `GeneralizedAdditiveModelBinaryClassifier`
 * Multiclass classification: `StochasticDualCoordinateAscentClassifier`, `LogisticRegressor`, and`NaiveBayesClassifier`
 * Regression: `FastTreeRegressor`, `FastTreeTweedieRegressor`, `StochasticDualCoordinateAscentRegressor`, `OrdinaryLeastSquaresRegressor`, `OnlineGradientDescentRegressor`, `PoissonRegressor`, and `GeneralizedAdditiveModelRegressor`

* Evaluators (to check how well the model works):
 * For Binary classification: `BinaryClassificationEvaluator`
 * For Multiclass classification: `ClassificationEvaluator`
 * For Regression: `RegressionEvaluator`

Additional components have been included in the repository but cannot be used in the `LearningPipeline` yet (this will be updated in future releases).

, '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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Add release notes for ML.NET 0.1#37

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 Merged

shauheen
merged 3 commits into

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May 7, 2018
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Add release notes for ML.NET 0.1
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6c93efe

Add release notes for ML.NET 0.1

May 6, 2018
d4688ad

Merge remote-tracking branch 'upstream/master'

May 6, 2018
3530b52

Release note tweaks

May 7, 2018

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Documentation/release-notes/0.1/release-0.1.md

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Original file line numberDiff line numberDiff line change
@@ -0,0 +1,47 @@
# ML.NET 0.1 Release Notes

ML.NET 0.1 is the first preview release of ML.NET. Thank you for trying it out and we look forward to your feedback! Try training, scoring, and using machine learning models in your app and tell us how it goes.

### Installation

ML.NET works on any platform that supports [.NET Core 2.0](https://www.microsoft.com/net/learn/get-started/windows). It also works on the .NET Framework.

You can install ML.NET NuGet from the .NET Core CLI using:
```
dotnet add package Microsoft.ML
```

From package manager:
```
Install-Package Microsoft.ML
```

Or from within Visual Studio's NuGet package manager.

### Release Notes

This initial release contains core ML.NET components for enabling machine learning pipelines:

* ML Data Structures (e.g. `IDataView`, `LearningPipeline`)

* TextLoader (loading data from a delimited text file into a `LearningPipeline`)

* Transforms (to get data in the correct format for training):
 * Processing/featurizing text: `TextFeaturizer`
 * Schema modifcation: `ColumnConcatenator`, `ColumnSelector`, and `ColumnDropper`
 * Working with categorical features: `CategoricalOneHotVectorizer` and `CategoricalHashOneHotVectorizer`
 * Dealing with missing data: `MissingValueHandler`
 * Filters: `RowTakeFilter`, `RowSkipFilter`, `RowRangeFilter`
 * Feature selection: `FeatureSelectorByCount` and `FeatureSelectorByMutualInformation`

* Learners (to train machine learning models) for a variety of tasks:
 * Binary classification: `FastTreeBinaryClassifier`, `StochasticDualCoordinateAscentBinaryClassifier`, `AveragedPerceptronBinaryClassifier`, `BinaryLogisticRegressor`, `FastForestBinaryClassifier`, `LinearSvmBinaryClassifier`, and `GeneralizedAdditiveModelBinaryClassifier`
 * Multiclass classification: `StochasticDualCoordinateAscentClassifier`, `LogisticRegressor`, and`NaiveBayesClassifier`
 * Regression: `FastTreeRegressor`, `FastTreeTweedieRegressor`, `StochasticDualCoordinateAscentRegressor`, `OrdinaryLeastSquaresRegressor`, `OnlineGradientDescentRegressor`, `PoissonRegressor`, and `GeneralizedAdditiveModelRegressor`

* Evaluators (to check how well the model works):
 * For Binary classification: `BinaryClassificationEvaluator`
 * For Multiclass classification: `ClassificationEvaluator`
 * For Regression: `RegressionEvaluator`

Additional components have been included in the repository but cannot be used in the `LearningPipeline` yet (this will be updated in future releases).

, '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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shauheen
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Add release notes for ML.NET 0.1
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Add release notes for ML.NET 0.1

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d4688ad

Merge remote-tracking branch 'upstream/master'

May 6, 2018
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May 7, 2018

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Documentation/release-notes/0.1/release-0.1.md

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# ML.NET 0.1 Release Notes

ML.NET 0.1 is the first preview release of ML.NET. Thank you for trying it out and we look forward to your feedback! Try training, scoring, and using machine learning models in your app and tell us how it goes.

### Installation

ML.NET works on any platform that supports [.NET Core 2.0](https://www.microsoft.com/net/learn/get-started/windows). It also works on the .NET Framework.

You can install ML.NET NuGet from the .NET Core CLI using:
```
dotnet add package Microsoft.ML
```

From package manager:
```
Install-Package Microsoft.ML
```

Or from within Visual Studio's NuGet package manager.

### Release Notes

This initial release contains core ML.NET components for enabling machine learning pipelines:

* ML Data Structures (e.g. `IDataView`, `LearningPipeline`)

* TextLoader (loading data from a delimited text file into a `LearningPipeline`)

* Transforms (to get data in the correct format for training):
 * Processing/featurizing text: `TextFeaturizer`
 * Schema modifcation: `ColumnConcatenator`, `ColumnSelector`, and `ColumnDropper`
 * Working with categorical features: `CategoricalOneHotVectorizer` and `CategoricalHashOneHotVectorizer`
 * Dealing with missing data: `MissingValueHandler`
 * Filters: `RowTakeFilter`, `RowSkipFilter`, `RowRangeFilter`
 * Feature selection: `FeatureSelectorByCount` and `FeatureSelectorByMutualInformation`

* Learners (to train machine learning models) for a variety of tasks:
 * Binary classification: `FastTreeBinaryClassifier`, `StochasticDualCoordinateAscentBinaryClassifier`, `AveragedPerceptronBinaryClassifier`, `BinaryLogisticRegressor`, `FastForestBinaryClassifier`, `LinearSvmBinaryClassifier`, and `GeneralizedAdditiveModelBinaryClassifier`
 * Multiclass classification: `StochasticDualCoordinateAscentClassifier`, `LogisticRegressor`, and`NaiveBayesClassifier`
 * Regression: `FastTreeRegressor`, `FastTreeTweedieRegressor`, `StochasticDualCoordinateAscentRegressor`, `OrdinaryLeastSquaresRegressor`, `OnlineGradientDescentRegressor`, `PoissonRegressor`, and `GeneralizedAdditiveModelRegressor`

* Evaluators (to check how well the model works):
 * For Binary classification: `BinaryClassificationEvaluator`
 * For Multiclass classification: `ClassificationEvaluator`
 * For Regression: `RegressionEvaluator`

Additional components have been included in the repository but cannot be used in the `LearningPipeline` yet (this will be updated in future releases).

, '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); }
})();
})();

Skip to content
Add release notes for ML.NET 0.1#37

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 Merged

shauheen
merged 3 commits into

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May 7, 2018
 Merged


Add release notes for ML.NET 0.1
#37
 Changes from all commits
Commits

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3 commits


Select commit
Hold shift + click to select a range
6c93efe

Add release notes for ML.NET 0.1

May 6, 2018
d4688ad

Merge remote-tracking branch 'upstream/master'

May 6, 2018
3530b52

Release note tweaks

May 7, 2018

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Documentation/release-notes/0.1/release-0.1.md

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Original file line numberDiff line numberDiff line change
@@ -0,0 +1,47 @@
# ML.NET 0.1 Release Notes

ML.NET 0.1 is the first preview release of ML.NET. Thank you for trying it out and we look forward to your feedback! Try training, scoring, and using machine learning models in your app and tell us how it goes.

### Installation

ML.NET works on any platform that supports [.NET Core 2.0](https://www.microsoft.com/net/learn/get-started/windows). It also works on the .NET Framework.

You can install ML.NET NuGet from the .NET Core CLI using:
```
dotnet add package Microsoft.ML
```

From package manager:
```
Install-Package Microsoft.ML
```

Or from within Visual Studio's NuGet package manager.

### Release Notes

This initial release contains core ML.NET components for enabling machine learning pipelines:

* ML Data Structures (e.g. `IDataView`, `LearningPipeline`)

* TextLoader (loading data from a delimited text file into a `LearningPipeline`)

* Transforms (to get data in the correct format for training):
 * Processing/featurizing text: `TextFeaturizer`
 * Schema modifcation: `ColumnConcatenator`, `ColumnSelector`, and `ColumnDropper`
 * Working with categorical features: `CategoricalOneHotVectorizer` and `CategoricalHashOneHotVectorizer`
 * Dealing with missing data: `MissingValueHandler`
 * Filters: `RowTakeFilter`, `RowSkipFilter`, `RowRangeFilter`
 * Feature selection: `FeatureSelectorByCount` and `FeatureSelectorByMutualInformation`

* Learners (to train machine learning models) for a variety of tasks:
 * Binary classification: `FastTreeBinaryClassifier`, `StochasticDualCoordinateAscentBinaryClassifier`, `AveragedPerceptronBinaryClassifier`, `BinaryLogisticRegressor`, `FastForestBinaryClassifier`, `LinearSvmBinaryClassifier`, and `GeneralizedAdditiveModelBinaryClassifier`
 * Multiclass classification: `StochasticDualCoordinateAscentClassifier`, `LogisticRegressor`, and`NaiveBayesClassifier`
 * Regression: `FastTreeRegressor`, `FastTreeTweedieRegressor`, `StochasticDualCoordinateAscentRegressor`, `OrdinaryLeastSquaresRegressor`, `OnlineGradientDescentRegressor`, `PoissonRegressor`, and `GeneralizedAdditiveModelRegressor`

* Evaluators (to check how well the model works):
 * For Binary classification: `BinaryClassificationEvaluator`
 * For Multiclass classification: `ClassificationEvaluator`
 * For Regression: `RegressionEvaluator`

Additional components have been included in the repository but cannot be used in the `LearningPipeline` yet (this will be updated in future releases).