Global minima refer to the lowest point in the entire loss landscape of a function, representing the optimal set of parameters for a model in machine learning. Finding the global minima is crucial because it ensures that the model performs at its best by minimizing the loss function across all possible parameter configurations. This concept is directly connected to optimization techniques like gradient descent, which aim to find these minima by iteratively adjusting the parameters.
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