京东 11.11 红包
AlexNet paper 04
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AlexNet paper 03
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AlexNet paper 02
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CNN: 4.4 triplet loss
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AlexNet paper 07
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CNN: 4.4 triplet loss part 2
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AlexNet paper 01
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CNN: 3.8 Anchor boxes part1
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VGG05
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CNN: object detection (car)
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why looks at case studies
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VGG01
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2.07 Inception network
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VGG02
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VGG 11
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CNN: 4.2 one shot learning part1
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作业解读CNN3:YOLO non-max suppression
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VGG 09
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VGG 07
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saturating vs non-saturating non-linearities
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CNN: 4.2 one shot learning part2
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CNN: 3.10 R-CNN region proposal part1
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CNN: 4.11 从2D到1D3D
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data augmentation
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CNN: 4.5 face verification and binary classification
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2.05 network in network and 1x1 convolution
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CNN: convolutional sliding window implementation 01
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CNN: landmark detection
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掰开揉碎CNN: pooling layer，strided convolution
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CNN: 3.10 R-CNN region proposal part 2
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CNN掰开揉碎：conv_layer, filter, feature map
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吴恩达CNN1.9池化层pooling layer 笔记
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2.04 Why ResNet works
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classic CNN models
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2.06 谷歌inception网络介绍
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作业解析CNN4.1: neural style transfer 02
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解读作业CNN2.2: 如何构建convolutional block
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1.1 计算机视觉：问题和难点
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吴恩达CNN1.4 Padding
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CNN: 4.9 content loss function
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(中文) 掰开揉碎：initialization, covariance shift, batch normalization