Deep learning II - I Practical aspects of deep learning - Understanding dropout 理解隨機失活正則化
Understanding dropout 解釋 1 通過dropout，每次訓練都構建一個更小的神經網絡，從而減小過擬合現象發生 解釋 2 通過dropout，每個neuron不能依賴前一層的某個特定的feature，所以不得不將權重值分散開，從而起到了shrink weights的作用，從而減小過擬合。 注意 dropout使得function J J 不在能用來做debug的工具
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相關文章
- 1. Deep learning II - I Practical aspects of deep learning - Dropout regularization 隨機失活正則化
- 2. Deep learning II - I Practical aspects of deep learning - Normalizing inputs 輸入歸一化
- 3. Deep learning II - I Practical aspects of deep learning - 正則化如何較少過擬合
- 4. Deep learning II - I Practical aspects of deep learning - other regularization methods 其他正則化方法
- 5. Deep learning II - I Practical aspects of deep learning - Vanishing/Exploring gradients 梯度消失/爆炸
- 6. 007 Practical aspects of Deep Learning
- 7. Deep learning II - I Practical aspects of deep learning - Regularizing your neural network 神經網絡範數正則化
- 8. [coursera/ImprovingDL/week1]Practical aspects of Deep Learning(summary&question)
- 9. 【吳恩達深度學習筆記】2.1 Practical aspects of Deep Learning
- 10. Deep Learning：正則化（十二）