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* **Activation Functions**: After each convolutional layer, an activation function is applied. This function introduces non-linearity, allowing the network to learn more complex patterns. Common activation functions include ReLU (Rectified Linear Unit), which simply sets negative values to zero, and sigmoid and tanh functions. The choice of activation function impacts how quickly the network learns and the complexity of the patterns it can capture.
