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Sigmoid redundancy, often referred to in the context of neural networks, particularly relates to issues like vanishing gradients when using sigmoid activation functions. While it cannot be "cured" per se, it can be mitigated by employing alternative activation functions like ReLU (Rectified Linear Unit) or using techniques such as batch normalization. Additionally, architectural changes, like deeper networks or residual connections, can help reduce the impact of redundancy in training. Ultimately, the choice of architecture and activation function plays a crucial role in addressing these issues.

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