Understanding the Role of Hyperparameters #ai #artificialintelligence #machinelearning #aiagent

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Hyperparameters are predefined variables that control the learning process and influence the behavior of machine learning algorithms. Unlike parameters, which the model learns from the data, hyperparameters are set before training and determine the structure and functioning of the model. Examples include learning rate, batch size, and the number of layers in a neural network. Choosing the right hyperparameters can be the key to unlocking the full potential of your models. They directly affect the model’s ability to generalize from the training data to unseen data. As machine learning practitioners, it's crucial to understand their role and impact on model performance.