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Learning Microsoft Cognitive Services - Third Edition

By : Leif Larsen Henning Larsen

Learning Microsoft Cognitive Services

By: Leif Larsen Henning Larsen

Overview of this book

Microsoft Cognitive Services is a set of APIs for integrating artificial intelligence in your applications to solve logical business problems. If you’re new to developing applications with AI, Learning Microsoft Cognitive Services will give you a comprehensive introduction to Microsoft’s AI stack and get you up-to-speed in no time. The book introduces you to 24 APIs, including Emotion, Language, Vision, Speech, Knowledge, and Search. Using Visual Studio, you can develop applications with enhanced capabilities for image processing, speech recognition, text processing, and much more. Moving forward, you will work with datasets that enable your applications to process various data in the form of image, video, or text. By the end of the book, you’ll be able to confidently explore Cognitive Services APIs for building intelligent applications that can be deployed for real-world business uses.

Preface

Preface

Who this book is for

What this book covers

To get the most out of this book

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1. Getting Started with Microsoft Cognitive Services

1. Getting Started with Microsoft Cognitive Services

Cognitive Services in action for fun and life-changing purposes

Setting up the boilerplate code

Detecting faces with the Face API

An overview of different APIs

Getting feedback on detected faces

Summary

2. Analyzing Images to Recognize a Face

2. Analyzing Images to Recognize a Face

Analyze an image using the Computer Vision API

Diving deep into the Face API

Adding identification to our smart-house application

Knowing your mood using the Face API

Automatically moderating user content

Building your own image classifiers

Summary

3. Analyzing Videos

3. Analyzing Videos

Diving into Video Indexer

Unlocking video insights using Video Indexer

Summary

4. Letting Applications Understand Commands

4. Letting Applications Understand Commands

Creating language-understanding models

Training a model

Summary

5. Speaking with Your Application

5. Speaking with Your Application

Converting text to audio and vice versa

Knowing who is speaking

Verifying a person through speech

Customizing speech recognition

Translating speech on the fly

Summary

6. Understanding Text

6. Understanding Text

Setting up a common core

Correcting spelling errors

Extracting information through textual analysis

Translating text on the fly

Summary

7. Building Recommendation Systems for Businesses

7. Building Recommendation Systems for Businesses

Providing personalized recommendations

Summary

8. Querying Structured Data in a Natural Way

8. Querying Structured Data in a Natural Way

Tapping into academic content using the academic API

Interpreting natural language queries

Finding academic entities in 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

Answering FAQs using QnA Maker

Creating a knowledge base from frequently asked questions

Training the model

Publishing the model

Summary

9. Adding Specialized Searches

9. Adding Specialized Searches

Searching the web using the smart-house application

Getting the news

Searching for images and videos

Helping the user with autosuggestions

Search commonalities

Searching for visual content using Bing Visual Search

Adding a custom search

Summary

10. Connecting the Pieces

10. Connecting the Pieces

Completing our smart-house application

Real-life applications using Microsoft Cognitive Services

Where to go from here

Summary

A. LUIS Entities

A. LUIS Entities

LUIS prebuilt entities

B. License Information

B. License Information

Video Frame Analyzer

OpenCvSharp3

Newtonsoft.Json

NAudio

Index

Index

Correcting spelling errors

The Bing Spell Check API leverages the power of machine learning and statistical machine translation to train and evolve a highly contextual algorithm for spellchecking. Doing so allows us to utilize this to perform spellchecking using context.

A typical spellchecker will follow dictionary-based rule sets. As you can imagine, this will need continuous updates and expansions.

Using the Bing Spell Check API, we can recognize and correct slang and informal language. It can recognize common naming errors and correct word-breaking issues. It can detect and correct words that sound the same, but differ in meaning and spelling (homophones). It can also detect and correct brands and popular expressions.

Create a new View in the View folder; call the file SpellCheckView.xaml. Add a TextBox element for the input query. We will also need two TextBox elements for the pre- and post-context. Add a TextBox element to show the result and a Button element to execute the spellcheck...

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Tech Concepts

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Programming languages

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