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Add ML.NET Roadmap #30

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merged 5 commits into from
May 5, 2018
Merged

Add ML.NET Roadmap #30

merged 5 commits into from
May 5, 2018

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glebuk
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@glebuk glebuk commented May 5, 2018

Resolves issue #27
Changes

  • Adds roadmap
  • Adds folders for markdown docs
  • fixes a bug in readme to point to correct license file

glebuk added 3 commits May 4, 2018 16:51
Resolves ISSUE: #27
- Adds readmap.md
- Updates solution to include MD files.
- fixes missing link in readme.md
ROADMAP.md Outdated

### Featurization Improvements
* Text (*)
* Natural language text preprocessing such as tokenization, part-of-speech tagging, and sentence breaking
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@danmoseley danmoseley May 5, 2018

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Something is wonky with the bullets here, it isn't rendering right. Tabs/spaces? #Resolved

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@glebuk glebuk May 5, 2018

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What's the issue with the bullets? Can you be more specific?
I have two levels of indentations. They look ok for me on Github.


In reply to: 186246779 [](ancestors = 186246779)

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Replaced tabs with spaces just in case


In reply to: 186251594 [](ancestors = 186251594,186246779)

@glebuk glebuk merged commit cde0d7d into dotnet:master May 5, 2018
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@eerhardt eerhardt left a comment

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Anomaly detection is mentioned in both short and long term sections. Do we need it in both?

* Natural language text preprocessing such as tokenization, part-of-speech tagging, and sentence breaking
* Pre-trained text models that can be used for extracting of semantic or sentiment features from text
* Image (*)
* Image preprocessing such as loading, resizing, and normalization if images
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Type-o: if => of

ROADMAP.md Outdated
* Hybrid training of pipelines containing both DNN and non-DNN predictors
* Additional ML tasks (*)
* _Recommendation_ - Is a problem that can be phrased a: "For a given user, predict the ratings this user would give to the items that they have not explicitly rated yet"
* _Anomaly Detection_, also known as _outlier detection_. It is a task to identify items, events or observations which do not conform to an expected pattern in the dataset. Typical examples are: detecting credit card fraud, medical problems or errors in text. Anomalies are also referred to as outliers, �novelties, noise, deviations and exceptions
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Anomaly detection is mentioned in the short term section as well. Is this a duplicate?

@markusweimer markusweimer mentioned this pull request May 7, 2018
@ghost ghost locked as resolved and limited conversation to collaborators Mar 31, 2022
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4 participants