Book Image

AI Blueprints

By : Dr. Joshua Eckroth, Eric Schoen
Book Image

AI Blueprints

By: Dr. Joshua Eckroth, Eric 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 (14 chapters)
AI Blueprints
Foreword
Contributors
Preface
Other Books You May Enjoy
Index

Summary


This chapter developed a recommendation system with a wide range of use cases. We looked at content-based filtering to find similar items based on the items' titles and descriptions, and more extensively at collaborative filtering, which considers users' interests in the items rather than the items' content. Since we focused on implicit feedback, our collaborative filtering recommendation system does not need user ratings or other numeric scores to represent user preferences. Only passive data collection suffices to generate enough knowledge to make recommendations. Such passive data may include purchases, listens, clicks, and so on.

After collecting data for some users, along with their purchase/listen/click patterns, we used matrix factorization to represent how users and items relate and to reduce the size of the data. The implicit and faiss libraries are used to make an effective recommendation system, and the Flask library is used to create a simple HTTP API that is general purpose...