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AI Blueprints

AI Blueprints

By : Dr. Joshua Eckroth , Schoen
5 (14)
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AI Blueprints

AI Blueprints

5 (14)
By: Dr. Joshua Eckroth , Schoen

Overview of this book

AI Blueprints gives you a working framework and the techniques to build your own successful AI business applications. You’ll learn across six business scenarios how AI can solve critical challenges with state-of-the-art AI software libraries and a well thought out workflow. Along the way you’ll discover the practical techniques to build AI business applications from first design to full coding and deployment. The AI blueprints in this book solve key business scenarios. The first blueprint uses AI to find solutions for building plans for cloud computing that are on-time and under budget. The second blueprint involves an AI system that continuously monitors social media to gauge public feeling about a topic of interest - such as self-driving cars. You’ll learn how to approach AI business problems and apply blueprints that can ensure success. The next AI scenario shows you how to approach the problem of creating a recommendation engine and monitoring how those recommendations perform. The fourth blueprint shows you how to use deep learning to find your business logo in social media photos and assess how people interact with your products. Learn the practical techniques involved and how to apply these blueprints intelligently. The fifth blueprint is about how to best design a ‘trending now’ section on your website, much like the one we know from Twitter. The sixth blueprint shows how to create helpful chatbots so that an AI system can understand customers’ questions and answer them with relevant responses. This book continuously demonstrates a working framework and strategy for building AI business applications. Along the way, you’ll also learn how to prepare for future advances in AI. You’ll gain a workflow and a toolbox of patterns and techniques so that you can create your own smart code.
Table of Contents (11 chapters)
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10
Index

Method – sentiment analysis

Sentiment analysis is achieved by labeling individual words as positive or negative, among other possible sentiments such as happy, worried, and so on. The sentiment of the sentence or phrase as a whole is determined by a procedure that aggregates the sentiment of individual words. Consider the sentence, I didn't like a single minute of this film. A simplistic sentiment analysis system would probably label the word like as positive and the other words as neutral, yielding an overall positive sentiment. More advanced systems analyze the "dependency tree" of the sentence to identify which words are modifiers for other words. In this case, didn't is a modifier for like, so the sentiment of like is reversed due to this modifier. Likewise, a phrase such as, It's definitely not dull, exhibits a similar property, and ...not only good but amazing exhibits a further nuance of the English language.

It is clear a simple dictionary of...

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