Applied Deep Learning Foundations for AI Practitioners
Lesson 2: Data, Features, Labels, and Training
Lesson Objectives
By the end of this lesson, students should be able to:
- Explain examples, labels, and features.
- Recognize inconsistent labels.
- Build a small practice dataset with edge cases.
Lesson Content
Training examples are practice cases. A label is the answer the system should learn. A feature is a useful signal from the input. In older systems, humans often designed features by hand. In deep learning, the model learns many internal features from data, but the quality of the data still matters.
Bad labels teach bad habits. If one reviewer labels "I cannot log in" as technical and another labels it as account, the model receives mixed signals. Edge cases matter too. A message can mention billing and technical trouble at the same time. The team must decide which label wins or whether the system should allow more than one label.
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