Deep learning II - I Practical aspects of deep learning - Dropout regularization 隨機失活正則化
Dropout regularization 隨機失活正則化 在訓練神經網絡時，隨機在每層選擇一定數量的neuron關閉，從而達到減小過擬合的目的。看起來很瘋狂，但是很有效。 1. 對應每一層生成以個mask d[3] d [ 3 ] 2. 用mask與該層的neurons a[3] a [ 3 ] 覆蓋，dropout一些neuron 3. 剩餘的neurons除以keep-prob，從而保持該
相關文章
- 1. Deep learning II - I Practical aspects of deep learning - Understanding dropout 理解隨機失活正則化
- 2. Deep learning II - I Practical aspects of deep learning - other regularization methods 其他正則化方法
- 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 - Normalizing inputs 輸入歸一化
- 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 神經網絡範數正則化
- 7. [coursera/ImprovingDL/week1]Practical aspects of Deep Learning(summary&question)
- 8. 007 Practical aspects of Deep Learning
- 9. 【吳恩達深度學習筆記】2.1 Practical aspects of Deep Learning
- 10. 深度神經網絡優化（一）- Practical aspects of Deep Learning
- 更多相關文章...
- • C# 正則表達式 - C#教程
- • Scala 正則表達式 - Scala教程
- • 漫談MySQL的鎖機制
- • IntelliJ IDEA代碼格式化設置
相關標籤/搜索
每日一句
-
每一个你不满意的现在，都有一个你没有努力的曾经。
歡迎關注本站公眾號,獲取更多信息
相關文章
- 1. Deep learning II - I Practical aspects of deep learning - Understanding dropout 理解隨機失活正則化
- 2. Deep learning II - I Practical aspects of deep learning - other regularization methods 其他正則化方法
- 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 - Normalizing inputs 輸入歸一化
- 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 神經網絡範數正則化
- 7. [coursera/ImprovingDL/week1]Practical aspects of Deep Learning(summary&question)
- 8. 007 Practical aspects of Deep Learning
- 9. 【吳恩達深度學習筆記】2.1 Practical aspects of Deep Learning
- 10. 深度神經網絡優化（一）- Practical aspects of Deep Learning