Table of Contents
Preface
Part 1: Real Data Issues, Limitations, and Challenges
1
Machine Learning and the Need for Data
Technical requirements
Artificial intelligence, machine learning, and deep learning
Artificial intelligence (AI)
Machine learning (ML)
Deep learning (DL)
Why are ML and DL so powerful?
Feature engineering
Transfer across tasks
Training ML models
Collecting and annotating data
Designing and training an ML model
Validating and testing an ML model
Iterations in the ML development process
Summary
2
Annotating Real Data
Annotating data for ML
Learning from data
Training your ML model
Testing your ML model
Issues with the annotation process
The annotation process is expensive
The annotation process is error-prone
The annotation ...
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