<ul data-eligibleForWebStory="false"><li>Neural networks face the vanishing gradient problem as gradients become very small during backpropagation through many layers.</li><li>Activation functions like Sigmoid and Tanh can lead to gradients close to zero, hindering weight updates in earlier layers.</li><li>Choosing the right activation function is crucial for the performance of deep learning models, with options like ReLU, Leaky ReLU, and Softmax addressing the vanishing gradient issue.</li><li>Understanding activation functions and their impact can assist in designing more effective and accurate neural networks.</li></ul>

Understanding Activation Functions and the Vanishing Gradient Problem

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