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AI Blueprints
AI Blueprints
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Foreword
Contributors
About the author
About the reviewer
Packt is searching for authors like you
Preface
Who this book is for
What this book covers
What you need for this book
Download the example code files
Download the color images
Conventions used
Get in touch
Reviews
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
2. A Blueprint for Planning Cloud Infrastructure
The problem, goal, and business case
Method – constraint solvers
OptaPlanner
Deployment strategy
Continuous evaluation
Summary
3. A Blueprint for 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
4. A Blueprint for 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
5. A Blueprint for 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
6. A Blueprint for 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
7. A Blueprint for 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
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
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Index
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