Convolutional Neural Networks：第二週
1. Classic Networks 第二週的課程首先介紹了幾種經典的卷積神經網絡模型： LeNet-5 AlexNet VGG 1.1 LeNet-5 LeNet分爲卷積層塊和全連接層塊兩個部分。卷積層塊裏的基本單位是卷積層後接最大池化層：卷積層用來識別圖片裏的空間模式，例如線條和物體局部，之後的最大池化層則用來降低卷積層對位置的敏感性。卷積層塊由兩個這樣的基礎塊構成，即兩個卷積層和兩個最大池
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相關文章
- 1. 第四課:Convolutional Neural Networks 第二週：編程作業:Residual Networks
- 2. Convolutional Neural Networks---Foundations of Convolutional Neural Networks
- 3. Convolutional Neural Networks筆記（二）
- 4. Convolutional Neural Networks: Application
- 5. C4:Convolutional Neural Networks
- 6. Convolutional Neural Networks-Week1
- 7. Convolutional Neural Networks
- 8. Orthogonal Convolutional Neural Networks
- 9. Convolutional Neural Networks-Week2
- 10. Convolutional Neural Networks - Week4