京东 11.11 红包
CNN: 4.4 triplet loss part3
发布人
打开封面 下载高清视频 观看高清视频 视频下载器
...
CNN: 4.4 triplet loss part 2
...
CNN: 4.4 triplet loss
...
CNN: 4.11 从2D到1D3D
...
CNN: 3.10 R-CNN region proposal part1
...
VGG 11
...
VGG 07
...
VGG01
...
CNN: object detection (car)
...
吴恩达CNN1.9池化层pooling layer 笔记
...
2.04 Why ResNet works
...
2.07 Inception network
...
吴恩达CNN1.3更多边缘检测
...
AlexNet paper 06
...
掰开揉碎CNN: pooling layer，strided convolution
...
吴恩达CNN1.4 Padding
...
AlexNet paper 07
...
why looks at case studies
...
AlexNet paper 01
...
什么是strided convolution? 跳出格怎么处理？
...
吴恩达CNN 1.2 Edge Detection Example 笔记
...
2.03 残差网络 Residual network
...
吴恩达表示，他看到的最令人兴奋的趋势是，生成式人工智能使人们能够以前所未有的水平创建软件，推动事物发展的速度比以往任何时候都快
...
CNN: 3.10 R-CNN region proposal part 2
...
CNN: convolutional sliding window implementation 01
...
AlexNet paper 02
...
作业解读CNN3:YOLO non-max suppression
...
CNN: convolutional implementation of sliding window 02
...
CNN: landmark detection
...
解读作业CNN2.2: 如何构建convolutional block
...
吴恩达机器学习图解笔记
...
tangent library
...
CNN: 4.6 neural style transfer
...
1.8 简单卷积网络示例 simple CNN
...
作业解读CNN3: YOLO 从output到target
...
Batch Normalization at test time
...
CNN掰开揉碎：conv_layer, filter, feature map
...
VGG02
...
CNN_Newsroom_20241029
...
VGG 10
...
为什么当learning_rate 变大如>= 0.01 时，loss为NaN？