<ul><li>The vanishing gradient problem occurs in deep neural networks when gradients become very small, halting the learning process in certain layers.</li><li>Activation functions like sigmoid or tanh can contribute to the vanishing gradient problem by mapping large input values to small output ranges.</li><li>Strategies to address the vanishing gradient problem include using the Rectified Linear Unit (ReLU) activation function, batch normalization, gradient clipping, and residual networks.</li><li>Innovations in activation functions, layer design, and normalization techniques have allowed for training deeper networks and overcoming the vanishing gradient problem.</li></ul>

Understanding the Vanishing Gradient Problem in Deep Learning

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