Book Image

The Applied AI and Natural Language Processing Workshop

By : Krishna Sankar, Jeffrey Jackovich, Ruze Richards
Book Image

The Applied AI and Natural Language Processing Workshop

By: Krishna Sankar, Jeffrey Jackovich, Ruze Richards

Overview of this book

Are you fascinated with applications like Alexa and Siri and how they accurately process information within seconds before returning accurate results? Are you looking for a practical guide that will teach you how to build intelligent applications that can revolutionize the world of artificial intelligence? The Applied AI and NLP Workshop will take you on a practical journey where you will learn how to build artificial intelligence (AI) and natural language processing (NLP) applications with Amazon Web services (AWS). Starting with an introduction to AI and machine learning, this book will explain how Amazon S3, or Amazon Simple Storage Service, works. You’ll then integrate AI with AWS to build serverless services and use Amazon’s NLP service Comprehend to perform text analysis on a document. As you advance, the book will help you get to grips with topic modeling to extract and analyze common themes on a set of documents with unknown topics. You’ll also work with Amazon Lex to create and customize a chatbot for task automation and use Amazon Rekognition for detecting objects, scenes, and text in images. By the end of The Applied AI and NLP Workshop, you’ll be equipped with the knowledge and skills needed to build scalable intelligent applications with AWS.
Table of Contents (8 chapters)
Preface

Face Comparison

Rekognition allows you to compare faces in two images. This is mainly for the purpose of identifying which faces are the same in both images. As an example use case, this can also be used for comparing images with people against their
personnel photo.

This section demonstrates industry standards so that you can utilize Amazon Rekognition to analyze faces inside an arrangement of pictures with different faces in them. When you indicate a Reference face (source) and a Comparison face (target) picture, Rekognition thinks about the biggest face in the source picture (that is, the reference) with up to 100 countenances recognized in the objective picture (that is, the examination images) and, after that, discovers how intently the face in the source picture matches with the appearances in the target picture. The closeness score for every examination is shown in the Results sheet.

Some restrictions on the usage of this feature are as follows:

  • If the source image...