Applied Deep Learning Foundations for AI Practitioners
Lesson 3: Evaluation, Overfitting, and Debugging
Lesson Objectives
By the end of this lesson, students should be able to:
- Explain training, validation, and test examples in plain language.
- Recognize overfitting and data leakage.
- Create a basic error review table.
Lesson Content
Evaluation answers the question: "Does this work on examples the system has not already practiced?" If a model only performs well on familiar examples, it may be memorizing surface patterns. That is overfitting.
A practical evaluation set should include new examples, edge cases, and examples that represent the people or situations the system will actually serve. Data leakage happens when the answer is accidentally included in the input or when test examples are too similar to training examples.
Debugging is not just changing settings. A professional review looks at wrong answers and asks why they failed. Was the label unclear? Was the input missing context? Was the example unlike training data? Was the task too broad?
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