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 demonstrated a variety of techniques for discovering trends and recognizing anomalies. These two outcomes, trends and anomalies, are related because they both rely on a model that describes the behavior or characteristics of the training data. For discovering trends, we fit a model and then query that model to find data streams that are dramatically increasing or decreasing in recent history. For anomaly detection, we can use the model to forecast the next observation and then check whether the true observation significantly differed from the forecast, or we can query the model to see how normal or abnormal a new observation is compared to the training data. How these techniques are deployed in practice depends on the technique used and the use case, but often one will train a model on recent data (say, the prior 90 days), while taking care to ensure the training data is not tarnished with anomalous data points that can undermine the model's ability to accurately detect...