<ul><li>Vanishing gradients occur when gradients become very small as they propagate through a deep network.</li><li>This leads to early layers receiving little to no signal and impairs learning.</li><li>Vanishing gradients are common in networks with sigmoid or tanh activations, many layers, and poorly chosen initial weights.</li><li>To mitigate vanishing gradients, techniques like ReLU activation, batch normalization, proper weight initialization, and skip connections are recommended.</li></ul>

Vanishing Gradients: Why Deep Networks Sometimes Forget

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