Download to read offline
![@Sherrrylst
Face API – Mood Analyzer
async runFaceDetect() {
this.dataSource = [];
const imageAsset = await takePicture(
{
width: 300,
height: 500,
keepAspectRatio: true,
saveToGallery: false
});
const filePath = await this.getFilePath(imageAsset);
const imageFile = File.fromPath(filePath);
this.pictureFromCamera = imageAsset;
this.result = await this.sendRequest(imageFile);
this.dataSource = getEmotionData(this.result);
}
Source code](https://image.slidesharecdn.com/dotnet2019-190619050955-190731070625/85/DotNet-2019-Sherry-List-Azure-Cognitive-Services-with-Native-Script-62-320.jpg)
![@Sherrrylst
Face API – Posting data
async runFaceDetect() {
this.dataSource = [];
const imageAsset = await takePicture(
{
width: 300,
height: 500,
keepAspectRatio: true,
saveToGallery: false
});
const filePath = await this.getFilePath(imageAsset);
const imageFile = File.fromPath(filePath);
this.pictureFromCamera = imageAsset;
this.result = await this.sendRequest(imageFile);
this.dataSource = getEmotionData(this.result);
}
Source code](https://image.slidesharecdn.com/dotnet2019-190619050955-190731070625/85/DotNet-2019-Sherry-List-Azure-Cognitive-Services-with-Native-Script-65-320.jpg)
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DotNet 2019 | Sherry List - Azure Cognitive Services with Native Script
This document provides an overview of Azure Cognitive Services presented by Sherry List. It begins with introductions and then covers key topics including artificial intelligence, machine learning, machine learning techniques like deep learning and clustering. It discusses how machine learning works with data, patterns, algorithms, models and training. Finally, it provides a detailed breakdown of the various cognitive services for decision, speech, language, search and vision with examples of APIs within each category. It also demonstrates how to use cognitive services by creating an account, calling REST APIs and parsing JSON responses.
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DotNet 2019 | Sherry List - Azure Cognitive Services with Native Script
- 1. Azure Cognitive Services: MoodAnalyzer
- 2. Hello! I’m Sherry List AzureDeveloper Technical Lead, Microsoft You can find me at @SherrryLst
- 3. AI, ML, DL,ANN? @Sherrrylst
- 4. Artificial Intelligence AI isthe simulation of human intelligence processes by machines. These processes include, reasoning, remembering, learning and self- correction. @Sherrrylst
- 5. Machine Learning @Sherrrylst Machine learningis a field of computer science that gives computers the ability to learn without being explicitly programmed. Arthur Samuel, 1959
- 6. Machine learning techniques @Sherrrylst •Artificial Neural Networks • Deep Learning • Bayesian Networks • Clustering
- 7. Artificial Intelligence (AI) Thebigger picture Machine Learning Artificial Neural Networks Deep Learning Bayesian Networks Clustering @Sherrrylst
- 8.
- 9.
- 10.
- 11. @Sherrrylst Data Patterns Algorithm (Deep learning, clustering,…) Machine Learning 101
- 12. @Sherrrylst Data Patterns Algorithm (Deep learning, clustering,…) Find patterns Machine Learning 101
- 13. @Sherrrylst Data Patterns Model Algorithm (Deep learning, clustering,…) Find patterns Machine Learning 101
- 14. @Sherrrylst Data Patterns Model Algorithm (Deep learning, clustering,…) Find patterns Recognizes patterns Machine Learning 101
- 15. @Sherrrylst Data Patterns Model Algorithm (Deep learning, clustering,…) Find patterns Recognizes patterns Training Machine Learning 101
- 16. @Sherrrylst Data Patterns Model Algorithm (Deep learning, clustering,…) Find patterns Recognizes patterns Application Training Machine Learning 101
- 17. @Sherrrylst Data Patterns Model Algorithm (Deep learning, clustering,…) Find patterns Recognizes patterns Application Challenges Data preparation
- 18. @Sherrrylst Data Patterns Model Algorithm (Deep learning, clustering,…) Find patterns Recognizes patterns Application Challenges Data preparation Create and test algorithms
- 19. @Sherrrylst Algorithm (Deep learning, clustering,…) Find patterns Challenges Create and test algorithms Chihuahua or muffin?
- 20. @Sherrrylst Data Patterns Model Algorithm (Deep learning, clustering,…) Find patterns Recognizes patterns Application Challenges Data preparation Create and test algorithms Expose the model to app
- 21. Is there abetter way? @Sherrrylst
- 22.
- 23. @Sherrrylst Application The easier way AzureCognitive Services REST API
- 24. Azure Cognitive Services=== machine-learning-as-a-service @Sherrrylst
- 25. @Sherrrylst Application The easier way AzureCognitive Services REST API
- 26. Azure Cognitive Services DecisionSpeech Language Search Vision @Sherrrylst
- 27.
- 28. @Sherrrylst • Content Moderator CognitiveServices - Decision
- 29. @Sherrrylst • Content Moderator •Anomaly Detector (Preview) Cognitive Services - Decision
- 30. @Sherrrylst • Content Moderator •Anomaly Detector (Preview) • Personalizer(Preview) Cognitive Services - Decision
- 31.
- 32. • Speech Services •Speech to Text • Text to Speech • Business Scenario (Call center) @Sherrrylst Cognitive Services - Speech
- 33. • Speech Services •Speaker recognition (Preview) @Sherrrylst Cognitive Services - Speech
- 34.
- 35. • Language Understanding @Sherrrylst CognitiveServices - Language
- 36. • Language Understanding •Bing Spell Check @Sherrrylst Cognitive Services - Language
- 37. • Language Understanding •Bing Spell Check • Translator Text @Sherrrylst Cognitive Services - Language
- 38. • Language Understanding •Bing Spell Check • Translator Text • Text Analytics @Sherrrylst Cognitive Services - Language
- 39. • Language Understanding •Bing Spell Check • Translator Text • Text Analytics • QnA Maker @Sherrrylst Cognitive Services - Language
- 40.
- 41. Cognitive Services -Search @Sherrrylst • Bing Custom Search • Bing Web Search • Bing Video Search • Bing Image Search • Bing Local Business Search (Preview) • Bing Visual Search • Bing Entity Search • Bing News Search • Bing Auto Suggest
- 42.
- 43. • Computer vision @Sherrrylst CognitiveServices - Vision
- 44. • Computer vision •Video Indexer @Sherrrylst Cognitive Services - Vision
- 45. • Computer vision •Video Indexer • Face @Sherrrylst Cognitive Services - Vision
- 46. • Computer vision •Video Indexer • Face • Custom Vision @Sherrrylst Cognitive Services - Vision
- 47. • Computer vision •Video Indexer • Face • Custom Vision • Ink Recognizer (Preview) @Sherrrylst Cognitive Services - Vision
- 48. • Computer vision •Video Indexer • Face • Custom Vision • Ink Recognizer (Preview) • Form Recognizer (Preview) @Sherrrylst Cognitive Services - Vision
- 49. How to useit? @Sherrrylst
- 50. How to useCognitive Services? 1. Create service in Azure Portal (https://portal.azure.com/) @Sherrrylst
- 51. How to useCognitive Services? 1. Create service in Azure Portal (https://portal.azure.com/) 2. Customize and train model in the portal (Optional) @Sherrrylst
- 52. How to useCognitive Services? 1. Create service in Azure Portal (https://portal.azure.com/) 2. Customize and train model in the portal (Optional) 3. Call the REST API from your app @Sherrrylst
- 53. How to useCognitive Services? 1. Create service in Azure Portal (https://portal.azure.com/) 2. Customize and train model in the portal (Optional) 3. Call the REST API from your app 4. Parse the JSON response @Sherrrylst
- 54.
- 55. Create a CognitiveServices account in the Azure portal @Sherrrylst
- 56. @Sherrrylst Cognitive Services account- Face Face API
- 57.
- 58. @Sherrrylst • URL :{Endpoint}/face/v1.0/detect • URI Parameters • Location • returnFaceLandmarks (Boolean) • returnFaceAttributes • age,gender,smile,glasses,emotion,facialHair • Headers • Content-Type: application/octet-stream | application/json • Ocp-Apim-Subscription-Key Face API - Detect Face API Documentation
- 59. @Sherrrylst Face API -Detect Face API Documentation
- 60.
- 61.
- 62. @Sherrrylst Face API –Mood Analyzer async runFaceDetect() { this.dataSource = []; const imageAsset = await takePicture( { width: 300, height: 500, keepAspectRatio: true, saveToGallery: false }); const filePath = await this.getFilePath(imageAsset); const imageFile = File.fromPath(filePath); this.pictureFromCamera = imageAsset; this.result = await this.sendRequest(imageFile); this.dataSource = getEmotionData(this.result); } Source code
- 63. @Sherrrylst Face API –Posting data (Part 1) sendRequest(file: File): Promise<any> { return new Promise((resolve, reject) => { const ses = session('image-upload'); const request = { url: this.faceUrl, method: 'POST', headers: { 'Content-Type': 'application/octet-stream', 'Ocp-Apim-Subscription-Key': '7d30f6357121430e91b3f8439b8205c6', }, description: 'Uploading ' + file.name }; const task = ses.uploadFile(file.path, request); ... }); } Source code
- 64. @Sherrrylst Face API –Posting data (Part 2) sendRequest(file: File): Promise<any> { return new Promise((resolve, reject) => { ... task.on('error', e => reject('error ' + e.responseCode + ' code.') ) task.on('responded', e => { const data = JSON.parse(e.data); return resolve(data); } ); }); } Source code
- 65. @Sherrrylst Face API –Posting data async runFaceDetect() { this.dataSource = []; const imageAsset = await takePicture( { width: 300, height: 500, keepAspectRatio: true, saveToGallery: false }); const filePath = await this.getFilePath(imageAsset); const imageFile = File.fromPath(filePath); this.pictureFromCamera = imageAsset; this.result = await this.sendRequest(imageFile); this.dataSource = getEmotionData(this.result); } Source code
- 66.
- 67.
- 68.
- 69. Azure Cognitive Servicesre-cap @Sherrrylst • Easy to use (REST API) • Easy to provision • Provide pre-trained models • Possibility to use custom data • Free training resources • Detailed documentation • Create account for free
- 70.
- 71.
- 72.
- 73. Resources • Microsoft AzureCognitive Services: The Big Picture • The Mojifier (MS Learn) • The Mojifier (Github) • Azure Cognitive Services API - I need your clothes, boots and your motorcycle • Add conversational intelligence to your apps by using LUIS (MS Learn) • Discover sentiment in text with the Text Analytics API (MS Learn) • Create Intelligent Bots with the Azure Bot Service (MS Learn) • Getting started with Azure Cognitive Services