Residual networks are a type of neural network architecture designed to ease the training of very deep networks by introducing skip connections that allow gradients to flow through the network without vanishing. These skip connections help maintain the information across layers, allowing for improved performance in tasks like image recognition and natural language processing. They are essential in optimizing the training process and enhancing model accuracy.
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