AI at Microsoft
17 Mar 2018•0 j'aime•116 vues
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A quick introduction to AI solutions already available in the Microsoft plateform. Content presented to Montreal SharePoint and Office 365 UserGroups.
Nicolas GeorgeaultSuivre
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AI at Microsoft
- 4. Distinguishing Brain From Mind - The Atlantic
- 7. • Classification • Regression • Clustering • Association
- 10. Microsoft Cognitive Services Give your apps a human side
- 15. Roll your own with REST APIs Simple to add: just a few lines of code required Integrate into the language and platform of your choice Breadth of offerings helps you find the right API for your app Built by experts in their field from Microsoft Research, Bing, and Azure Machine Learning Quality documentation, sample code, and community support Easy Flexible Tested GET A KEY
- 18. Developer Resources Preview Pricing https://www.microsoft.com/cognitive-services/en-us/pricing Documentation https://www.microsoft.com/cognitive-services/en-us/computer-vision- api/documentation Client SDKs and Samples https://www.microsoft.com/cognitive-services/en-us/sdk-sample Join Our Community https://stackoverflow.com/questions/tagged/microsoft-cognitive https://social.msdn.microsoft.com/forums/azure/en- US/home?forum=mlapi https://cognitive.uservoice.com/
- 20. Get started for free at microsoft.com/cognitive
Notes de l'éditeur
- Supervised: All data is labeled and the algorithms learn to predict the output from the input data. Classification: A classification problem is when the output variable is a category, such as “red” or “blue” or “disease” and “no disease”. Regression: A regression problem is when the output variable is a real value, such as “dollars” or “weight”. Unsupervised: All data is unlabeled and the algorithms learn to inherent structure from the input data. Semi-supervised: Some data is labeled but most of it is unlabeled and a mixture of supervised and unsupervised techniques can be used.