Deep learning II - I Practical aspects of deep learning - Dropout regularization 随机失活正则化
Dropout regularization 随机失活正则化 在训练神经网络时，随机在每层选择一定数量的neuron关闭，从而达到减小过拟合的目的。看起来很疯狂，但是很有效。 1. 对应每一层生成以个mask d[3] d [ 3 ] 2. 用mask与该层的neurons a[3] a [ 3 ] 覆盖，dropout一些neuron 3. 剩余的neurons除以keep-prob，从而保持该
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相关文章
- 1. Deep learning II - I Practical aspects of deep learning - Understanding dropout 理解随机失活正则化
- 2. Deep learning II - I Practical aspects of deep learning - other regularization methods 其他正则化方法
- 3. Deep learning II - I Practical aspects of deep learning - 正则化如何较少过拟合
- 4. Deep learning II - I Practical aspects of deep learning - Normalizing inputs 输入归一化
- 5. Deep learning II - I Practical aspects of deep learning - Vanishing/Exploring gradients 梯度消失/爆炸
- 6. Deep learning II - I Practical aspects of deep learning - Regularizing your neural network 神经网络范数正则化
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