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Hyperparameters are the settings that you define before training a model. They’re not learned by the model itself — instead, you choose them manually or through tuning. Examples include learning rate, number of layers, and batch size. Mastering them is key to optimizing model performance! 👉 Subscribe for more AI/ML concepts in under 60 ...
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