AI blueprints : how to build and deploy AI business projects / Joshua Eckroth

Includes index

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 analysis.-- Deployment 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 Media.--The 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 Clustering.-- Deployment 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 things.-- Natural language processing.-- Computer vision.-- Expert systems and business rules.-- Planning and scheduling.-- Robotics.-- Understanding the hype cycle of AI.-- The next big thing.—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.