京东 11.11 红包
作业解析CNN2.2: 如何搭建identity block
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2.06 谷歌inception网络介绍
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作业解读CNN3:YOLO non-max suppression
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作业解读CNN2.1: 深模型的好处与弊端
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CNN2.1 作业解读（无答案）
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CNN: object detection (car)
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CNN: 4.2 one shot learning part1
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吴恩达CNN1.9池化层pooling layer 笔记
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作业解读CNN3: YOLO 从output到target
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2.04 Why ResNet works
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CNN: 4.4 triplet loss part 2
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AlexNet paper 03
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VGG04
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VGG 11
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掰开揉碎CNN: pooling layer，strided convolution
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作业解读CNN3: YOLO 数据样本特点
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CNN: landmark detection
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复习CNN: filter, feature map
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CNN掰开揉碎：conv_layer, filter, feature map
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AlexNet paper 04
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(EN) 第二课作业解析：regularization 02
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(中文) 掰开揉碎：initialization, covariance shift, batch normalization
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tangent library
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1.6 卷积中'卷'的体现之处 Convolution over volumes
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吴恩达CNN 1.2 Edge Detection Example 笔记
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plt.rcParams
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2.05 network in network and 1x1 convolution
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作业解读CNN4.2： 人脸识别
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AlexNet paper 02
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吴恩达CNN1.4 Padding
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AlexNet paper 06
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2.03 残差网络 Residual network
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CNN: 4.9 content loss function
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CNN: 4.2 one shot learning part2
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什么是strided convolution? 跳出格怎么处理？
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why looks at case studies
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CNN: convolutional sliding window implementation 01
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【人工神经网络】【准确率：93.03%】04 从零开始搭建一个人工神经网络（训练+测试）
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VGG 07
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1.7 单层卷积网络 one layer of convolutional network
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吴恩达机器学习图解笔记