Deep learning II - I Practical aspects of deep learning - 正則化如何較少過擬合
How regularization reduces overfitting? 解釋 1 通過regularization, 使得神經網絡各個層級的參數 W W 都更小更趨近於零，從而使得一個複雜的神經網絡變得更像一個簡單的神經網絡（當參數真爲零是，就是簡單的神經網絡了），從而減小過擬合。 解釋 2 以 tanh tanh 激勵函數爲例，當 w[l] w [ l ] 較小時，則 z[l]=w[l]
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相關文章
- 1. Deep learning II - I Practical aspects of deep learning - Normalizing inputs 輸入歸一化
- 2. Deep learning II - I Practical aspects of deep learning - Understanding dropout 理解隨機失活正則化
- 3. Deep learning II - I Practical aspects of deep learning - other regularization methods 其他正則化方法
- 4. Deep learning II - I Practical aspects of deep learning - Dropout regularization 隨機失活正則化
- 5. 007 Practical aspects of Deep Learning
- 6. Deep learning II - I Practical aspects of deep learning - Vanishing/Exploring gradients 梯度消失/爆炸
- 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. Deep Learning：正則化（十二）
- 10. [Deep Learning] 正則化