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Hyperparameters are settings not learned by the model but defined before training. They are crucial for anyone taking an AI Certificate Course because they directly affect how efficiently and accurately a model learns.
Examples include:
-
Learning rate
-
Number of layers
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Batch size
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Number of epochs
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Dropout rate
They significantly influence training performance, model stability, and overall accuracy.
Topic starter
Posted : 20/11/2025 5:38 am