Skip connections are shortcuts in neural network architectures that allow certain layers to bypass one or more intermediate layers, connecting directly to later layers. This design helps preserve important features and gradients during backpropagation, making it easier for the model to learn and improving overall performance. Skip connections play a crucial role in addressing the vanishing gradient problem and enabling deeper networks to train effectively.
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