1 - 2
- Larsen, Leif.
- Third edition. - Birmingham : Packt Publishing Ltd, 2018.
- Description
- Book โ 1 online resource
- Summary
-
- Table of Contents Getting Started with Microsoft Cognitive Services Analyzing Images to Recognize a Face Analyzing Videos Letting Applications Understand Commands Speaking with Your Application Understanding Text Extending Knowledge Based on Context Querying Structured Data in a Natural Way Adding Specialized Searches Connecting the Pieces Appendix A: LUIS Entities and Additional Information on Linguistic Analysis Appendix B: License Information.
- (source: Nielsen Book Data)
(source: Nielsen Book Data)
2. Learning Microsoft Cognitive Services [2017]
- Larsen, Leif.
- Packt Publishing, 2017.
- Description
- Book โ 1 online resource
- Summary
-
- Cover
- Copyright
- Credits
- About the Author
- About the Reviewer
- www.PacktPub.com
- Customer Feedback
- Table of Contents
- Preface
- Chapter 1: Getting Started with Microsoft Cognitive Services
- Cognitive Services in action for fun and life changing purposes
- Setting up boilerplate code
- Detecting faces with the Face API
- Overview of what we are dealing with
- Vision
- Computer Vision
- Emotion
- Face
- Video
- Speech
- Bing Speech
- Speaker Recognition
- Custom Recognition
- Language
- Bing Spell Check
- Language Understanding Intelligent Service (LUIS)
- Linguistic Analysis
- Text Analysis
- Web Language Model
- Knowledge
- Academic
- Entity Linking
- Knowledge Exploration
- Recommendations
- Search
- Bing Web Search
- Bing Image Search
- Bing Video Search
- Bing News Search
- Bing Autosuggest
- Getting feedback on detected faces
- Summary
- Chapter 2: Analyzing Images to Recognize a Face
- Learning what an image is about using Computer Vision API
- Setting up a chapter example project
- Generic image analysis
- Recognizing celebrities using domain models
- Utilizing Optical Character Recognition
- Generating image thumbnails
- Diving deep into the Face API
- Retrieving more information from the detected faces
- Deciding whether two faces belong to the same person
- Finding similar faces
- Grouping similar faces
- Adding identification to our Smart-House application
- Creating our Smart-House application
- Adding people to be identified
- Identifying a person
- Summary
- Chapter 3: Analyzing Videos
- Knowing your mood using the Emotion API
- Getting images from a web camera
- Letting the smart-house know your mood
- Diving into the Video API
- Video operations as common code
- Getting operation results
- Wiring up execution in the ViewModel.
- Detecting and tracking faces in videos
- Detecting motion
- Stabilizing shaky videos
- Generating video thumbnails
- Analyzing emotions in videos
- Summary
- Chapter 4: Letting Applications Understand Commands
- Creating language-understanding models
- Register an account and get a license key
- Creating an application
- Recognizing key data using entities
- Understanding what the user wants using intents
- Simplifying development using pre-built models
- Pre-built applications
- Training a model
- Training and publishing the model
- Connecting to the smart-house application
- Model improvement through active usage
- Visualizing performance
- Resolving performance problems
- Adding model features
- Adding labeled utterances
- Looking for incorrect utterance labels
- Changing the schema
- Active learning
- Executing operations based on commands
- Maintaining conversations from unclear utterances
- Completing actions from intents
- Action fulfillment
- Summary
- Chapter 5: Speak with Your Application
- Converting text to audio and vice versa
- Speaking to the application
- Letting the application speak back
- Audio output format
- Error codes
- Supported languages
- Utilizing LUIS based on spoken commands
- Knowing who is speaking
- Adding speaker profiles
- Enrolling a profile
- Identifying the speaker
- Verifying a person through speech
- Customizing speech recognition
- Creating a custom acoustic model
- Creating a custom language model
- Deploying the application
- Summary
- Chapter 6: Understanding Text
- Setting up a common core
- New project
- Web requests
- Data contracts
- Correcting spelling errors
- Natural Language Processing using the Web Language Model
- Breaking a word into several
- Generating the next word in a sequence of words.
- Learning if a word is likely to follow a sequence of words
- Learning if certain words is likely to appear together
- Extracting information through textual analysis
- Detecting language
- Extracting key phrases from text
- Learning if a text is positive or negative
- Exploring text using linguistic analysis
- Introduction to linguistic analysis
- Analyzing text from a linguistic viewpoint
- Summary
- Chapter 7: Extending Knowledge Based on Context
- Linking entities based on context
- Providing personalized recommendations
- Creating a model
- Importing catalog data
- Importing usage data
- Building a model
- Consuming recommendations
- Recommending items based on prior activities
- Summary
- Chapter 8: Querying Structured Data in a Natural Way
- Tapping into academic content using the Academic API
- Setting up an example project
- Interpreting natural language queries
- Finding academic entities from query expressions
- Calculating the distribution of attributes from academic entities
- Entity attributes
- Creating the backend using the Knowledge Exploration Service
- Defining attributes
- Adding data
- Building the index
- Understanding natural language
- Local hosting and testing
- Going for scale
- Hooking into Microsoft Azure
- Deploying the service
- Answering FAQs using QnA Maker
- Creating a knowledge base from frequently asked questions
- Training the model
- Publishing the model
- Improving the model
- Summary
- Chapter 9: Adding Specialized Searches
- Searching the Web from the Smart-House application
- Preparing the application for web searches
- Searching the Web
- Getting the news
- News from queries
- News from categories
- Trending news
- Searching for images and videos
- Using a common user interface
- Searching for images
- Searching for videos.
- Helping the user with auto suggestions
- Adding Autosuggest to the user interface
- Suggesting queries
- Search commonalities
- Languages
- Pagination
- Filters
- Safe search
- Freshness
- Errors
- Summary
- Chapter 10: Connecting the Pieces
- Connecting the pieces
- Creating an intent
- Updating the code
- Executing actions from intents
- Searching news on command
- Describing news images
- Real-life applications using Microsoft Cognitive Services
- Uber
- DutchCrafters
- CelebsLike.me
- Pivothead
- wearable glasses
- Zero Keyboard
- The common theme
- Where to go from here
- Summary
- Appendix A: LUIS Entities and Intents
- LUIS pre-built intents
- LUIS pre-built entities
- Appendix B: Additional Information on Linguistic Analysis
- Part-of-Speech Tags
- Phrase types
- Appendix C: License Information
- Video Frame Analyzer
- OpenCvSharp3
- Newtonsoft. Json
- NAudio
- Definitions
- Grant of Rights
- Conditions and Limitations
- Index.
(source: Nielsen Book Data)
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