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AI Blueprints : How to Build and Deploy AI Business Projects.

By: Material type: TextTextPublication details: Birmingham : Packt Publishing Ltd, 2018.Description: 1 online resource (251 pages)Content type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 1788997972
  • 9781788997973
Subject(s): Genre/Form: Additional physical formats: Print version:: AI Blueprints : How to Build and Deploy AI Business Projects.DDC classification:
  • 006.3 23
LOC classification:
  • Q335 .E257 2018
Online resources:
Contents:
Cover; Copyright; Mapt Upsell; Contributors; Table of Contents; Preface; Chapter 1: The AI Workflow; AI isn't everything; The AI workflow; Characterize the problem; Checklist; Develop a method; Checklist; Design a deployment strategy; Checklist; Design and implement a continuous evaluation; Checklist; Overview of the chapters; Summary; Chapter 2: Planning Cloud Infrastructure; The problem, goal, and business case; Method -- constraint solvers; OptaPlanner; Deployment strategy; Continuous evaluation; Summary; Chapter 3: Making Sense of Feedback; The problem, goal, and business case
Method -- sentiment analysisDeployment strategy; CoreNLP processing pipeline; Twitter API; The GATE platform; Reddit API; News API; Dashboard with plotly.js and Dash; Continuous evaluation; Retraining CoreNLP sentiment models; Summary; Chapter 4: Recommending Products and Services; Usage scenario -- implicit feedback; Content-based recommendations; Collaborative filtering recommendations; BM25 weighting; Matrix factorization; Deployment strategy; Continuous evaluation; Calculating precision and recall for BM25 weighting; Online evaluation of our recommendation system; Summary
Chapter 5: Detecting Your Logo in Social MediaThe rise of machine learning; Goal and business case; Neural networks and deep learning; Deep learning; Convolutions; Network architecture; Activation functions; TensorFlow and Keras; YOLO and Darknet; Continuous evaluation; Summary; Chapter 6: Discovering Trends and Recognizing Anomalies; Overview of techniques; Discovering linear trends; Discovering dynamic linear trends with a sliding window; Discovering seasonal trends; ARIMA; Dynamic linear models; Recognizing anomalies; Z-scores with static models; Z-scores with sliding windows; RPCA
ClusteringDeployment strategy; Summary; Chapter 7: Understanding Queries and Generating Responses; The problem, goal, and business case; Our approach; The Pokémon domain; The course advising domain; Method -- NLP + logic programming + NLG; NLP with Rasa; Logic programming with Prolog and tuProlog; Prolog unification and resolution; Using Prolog from Java with tuProlog; Pokémon in Prolog; Natural language generation with SimpleNLG; A second example -- college course advising; Continuous evaluation; Summary; Chapter 8: Preparing for Your Future and Surviving the Hype Cycle; Always one step ahead
The state of thingsNatural language processing; Computer vision; Expert systems and business rules; Planning and scheduling; Robotics; Understanding the hype cycle of AI; The next big thing; Summary; Other Books You May Enjoy; Leave a review -- let other readers know what you think; Index
Summary: This book shows how to build intelligent applications to solve business needs. Several paradigms of AI are covered, including deep learning, natural language processing, planning, and logic programming. Each project is developed with a business goal in mind and care is taken to address deployment and evaluation issues. Dr. Joshua Eckroth ...
Holdings
Item type Current library Collection Call number Status Date due Barcode Item holds
eBook eBook e-Library EBSCO Computers Available
Total holds: 0

Print version record.

Cover; Copyright; Mapt Upsell; Contributors; Table of Contents; Preface; Chapter 1: The AI Workflow; AI isn't everything; The AI workflow; Characterize the problem; Checklist; Develop a method; Checklist; Design a deployment strategy; Checklist; Design and implement a continuous evaluation; Checklist; Overview of the chapters; Summary; Chapter 2: Planning Cloud Infrastructure; The problem, goal, and business case; Method -- constraint solvers; OptaPlanner; Deployment strategy; Continuous evaluation; Summary; Chapter 3: Making Sense of Feedback; The problem, goal, and business case

Method -- sentiment analysisDeployment strategy; CoreNLP processing pipeline; Twitter API; The GATE platform; Reddit API; News API; Dashboard with plotly.js and Dash; Continuous evaluation; Retraining CoreNLP sentiment models; Summary; Chapter 4: Recommending Products and Services; Usage scenario -- implicit feedback; Content-based recommendations; Collaborative filtering recommendations; BM25 weighting; Matrix factorization; Deployment strategy; Continuous evaluation; Calculating precision and recall for BM25 weighting; Online evaluation of our recommendation system; Summary

Chapter 5: Detecting Your Logo in Social MediaThe rise of machine learning; Goal and business case; Neural networks and deep learning; Deep learning; Convolutions; Network architecture; Activation functions; TensorFlow and Keras; YOLO and Darknet; Continuous evaluation; Summary; Chapter 6: Discovering Trends and Recognizing Anomalies; Overview of techniques; Discovering linear trends; Discovering dynamic linear trends with a sliding window; Discovering seasonal trends; ARIMA; Dynamic linear models; Recognizing anomalies; Z-scores with static models; Z-scores with sliding windows; RPCA

ClusteringDeployment strategy; Summary; Chapter 7: Understanding Queries and Generating Responses; The problem, goal, and business case; Our approach; The Pokémon domain; The course advising domain; Method -- NLP + logic programming + NLG; NLP with Rasa; Logic programming with Prolog and tuProlog; Prolog unification and resolution; Using Prolog from Java with tuProlog; Pokémon in Prolog; Natural language generation with SimpleNLG; A second example -- college course advising; Continuous evaluation; Summary; Chapter 8: Preparing for Your Future and Surviving the Hype Cycle; Always one step ahead

The state of thingsNatural language processing; Computer vision; Expert systems and business rules; Planning and scheduling; Robotics; Understanding the hype cycle of AI; The next big thing; Summary; Other Books You May Enjoy; Leave a review -- let other readers know what you think; Index

This book shows how to build intelligent applications to solve business needs. Several paradigms of AI are covered, including deep learning, natural language processing, planning, and logic programming. Each project is developed with a business goal in mind and care is taken to address deployment and evaluation issues. Dr. Joshua Eckroth ...

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