Dropout是一种常用的正则化方法，用于减少神经网络的过拟合现象。它的基本思想是在训练神经网络的过程中，随机地将一部分神经元的输出值置为0，从而使得神经网络的结构变得不稳定，从而强制网络学习到更加加鲁棒的特征表示。 具体来说，Dropout在训练过程中，对于每个神经元以一定的的概率进行保留或者丢弃。这样可以减少特征检测器（隐层节点）间的相互作用，阻止了某些特征仅仅在其它特定特征下才有效果的情况。

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No child should face their challenges alone. Zero Dropout, in partnership with BIC, is here to say: we see you, we believe in you, and we’re committed to helping you succeed.

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