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Learning Microsoft Cognitive Services

Learning Microsoft Cognitive Services

By : Leif Larsen Henning Larsen
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Learning Microsoft Cognitive Services

Learning Microsoft Cognitive Services

By: Leif Larsen Henning Larsen
Buy this Book

Overview of this book

Take your app development to the next level with Learning Microsoft Cognitive Services. Using Leif's knowledge of each of the powerful APIs, you'll learn how to create smarter apps with more human-like capabilities. ? Discover what each API has to offer and learn how to add it to your app ? Study each AI using theory and practical examples ? Learn current API best practices
Table of Contents (14 chapters)
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Preface
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Preface
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What this book covers
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What you need for this book
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Who this book is for
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Conventions
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Reader feedback
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Customer support
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1
1. Getting Started with Microsoft Cognitive Services
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1. Getting Started with Microsoft Cognitive Services
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Cognitive Services in action for fun and life changing purposes
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Setting up boilerplate code
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Detecting faces with the Face API
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Overview of what we are dealing with
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Getting feedback on detected faces
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Summary
2
2. Analyzing Images to Recognize a Face
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2. Analyzing Images to Recognize a Face
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Learning what an image is about using Computer Vision API
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Diving deep into the Face API
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Adding identification to our Smart-House application
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Summary
3
3. Analyzing Videos
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3. Analyzing Videos
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Knowing your mood using the Emotion API
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Diving into the Video API
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Analyzing emotions in videos
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Summary
4
4. Letting Applications Understand Commands
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4. Letting Applications Understand Commands
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Creating language-understanding models
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Training a model
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Executing operations based on commands
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Summary
5
5. Speak with Your Application
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5. Speak with Your Application
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Converting text to audio and vice versa
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Knowing who is speaking
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Verifying a person through speech
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Customizing speech recognition
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Summary
6
6. Understanding Text
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6. Understanding Text
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Setting up a common core
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Correcting spelling errors
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Natural Language Processing using the Web Language Model
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Extracting information through textual analysis
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Exploring text using linguistic analysis
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Summary
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7. Extending Knowledge Based on Context
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7. Extending Knowledge Based on Context
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Linking entities based on context
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Providing personalized recommendations
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Summary
8
8. Querying Structured Data in a Natural Way
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8. Querying Structured Data in a Natural Way
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Tapping into academic content using the Academic API
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Creating the backend using the Knowledge Exploration Service
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Answering FAQs using QnA Maker
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Summary
9
9. Adding Specialized Searches
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9. Adding Specialized Searches
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Searching the Web from the Smart-House application
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Getting the news
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Searching for images and videos
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Helping the user with auto suggestions
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Search commonalities
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Summary
10
10. Connecting the Pieces
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10. Connecting the Pieces
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Connecting the pieces
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Real-life applications using Microsoft Cognitive Services
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Where to go from here
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Summary
Appendix A. LUIS Entities and Intents
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LUIS pre-built intents
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LUIS pre-built entities
Appendix B. Additional Information on Linguistic Analysis
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Part-of-Speech Tags
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Phrase types
Appendix C. License Information
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Appendix C. License Information
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Video Frame Analyzer
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OpenCvSharp3
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Newtonsoft.Json
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NAudio

Providing personalized recommendations

If you run an e-commerce site, a feature that is nice to have is recommendations. Using the Recommendation API, you can easily add this. Utilizing Microsoft Azure Machine Learning, the API can be trained to recognize items that should be recommended.

There are three common scenarios for recommendations, as follows:

  • Frequently Bought Together (FBT): FBT is the scenario where items that are often bought together with other items are recommended. An example of this is if you buy a mouse; the API will then recommend a keyboard.

  • Item to Item Recommendations (I2I): I2I is the scenario where certain items are often viewed after other items. Typically, this will be in form of people who visited this item also visited this other item.

  • Customer to Item Recommendations (U2I): U2I is the scenario where you utilize a customer's previous actions to recommend items. If you sell movies, you can recommend other movies based on a customer's previous movie choices.

The...

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