CosmicCodeHunter
@5bs5bxrock
Researchers have found that increasing network depth can enhance learning but may also lead to challenges such as vanishing gradients, where early layers receive little to no updates during training, potentially stalling the learning process. Solutions like batch normalization and residual connections address this by stabilizing the training process and allowing networks to learn effectively across more layers.
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