Deep learning II - I Practical aspects of deep learning - Regularizing your neural network 神經網絡範數正則化
Regularizing your neural network 神經網絡正則化 Logistic regression regularization 先用簡單的邏輯迴歸正則化作爲例子，因爲神經網絡的參數 W W 是2維的。 無正則 J(w,b)=1m∑i=1mL(y^(i)−y(i)) J ( w , b ) = 1 m ∑ i = 1 m L ( y ^ ( i ) − y ( i ) ) L
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相關文章
- 1. 007 Practical aspects of Deep Learning
- 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 - Understanding dropout 理解隨機失活正則化
- 5. Deep learning II - I Practical aspects of deep learning - other regularization methods 其他正則化方法
- 6. Deep learning II - I Practical aspects of deep learning - Dropout regularization 隨機失活正則化
- 7. 深度神經網絡優化（一）- Practical aspects of Deep Learning
- 8. [coursera/ImprovingDL/week1]Practical aspects of Deep Learning(summary&question)
- 9. Deep learning II - I Practical aspects of deep learning - Vanishing/Exploring gradients 梯度消失/爆炸
- 10. Deep Learning：正則化（十二）