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

The next big thing


In conclusion, we now look ahead at what might be coming in the next few years. It is clear DL is here to stay, given its dramatic and broad successes in many application domains. In the near future, expect DL to be applied to even more applications, particularly in healthcare and medicine. There is also significant research interest in connecting data of different modalities together with DL. For example, building models that can create text descriptions of images, or creating images from text descriptions. This kind of research aims to put more logic and structure in DL architectures, so it's more sophisticated than simple "input/output" pairs (for example, input = image, output = "cat"). For example, Zhu and Jiang recently reported success in training a system to understand relations like the person is next to the horse just by looking at a photo (Deep Structured Learning for Visual Relationship Detection, Zhu, Yaohui, and Shuqiang Jiang, Proceedings of the Thirty-Second...