A simple neural network uses a sequence to adjust synaptic weights during learning. If the learning rate decreases too slowly, which outcome occurs?

💡 Explanation

The network learns local minima because slow reduction of the learning rate prevents escaping local minima in the error surface; the Momentum algorithm is stalled. Therefore, the network fails to reach the optimal solution, rather than converging quickly or generalizing well.

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