A local minimum is a point in a function where the value is lower than that of its neighboring points, but not necessarily the lowest overall value in the entire function. In the context of optimization techniques for neural networks, finding local minima is crucial because these points can affect the learning process and the performance of the model. Neural networks often use optimization algorithms to navigate the error landscape in search of these minima, which helps to minimize the loss function.
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