This tutorial describes the important components of a learning algorithm: representation (what the model looks like), evaluation (how do we differentiate good models from bad ones), and optimization (what is our process for finding the good models among all the possible models).
Part of course:
Learning = Representation + Evaluation + Optimization
- Relation between representation, evaluation and optimization
- Examples for each of the components
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