# 第二周 深度卷积网络：实例探究（Deep convolutional models: case studies）

- [2.1 经典网络（Classic networks）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-si-men-ke-juan-ji-shen-jing-wang-luo-convolutional-neural-networks/convolutional-neural-networks/deep-convolutional-models-case-studies/22-jing-dian-wang-luoff08-classic-networks.md)
- [2.2 残差网络（Residual Networks (ResNets)）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-si-men-ke-juan-ji-shen-jing-wang-luo-convolutional-neural-networks/convolutional-neural-networks/deep-convolutional-models-case-studies/23-can-cha-wang-luo-ff08-residual-networks-resnets.md)
- [2.3 残差网络为什么有用？（Why ResNets work?）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-si-men-ke-juan-ji-shen-jing-wang-luo-convolutional-neural-networks/convolutional-neural-networks/deep-convolutional-models-case-studies/24-can-cha-wang-luo-wei-shi-yao-you-yong-ff1f-ff08-why-resnets-work.md)
- [2.4 网络中的网络以及 1×1 卷积（Network in Network and 1×1 convolutions）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-si-men-ke-juan-ji-shen-jing-wang-luo-convolutional-neural-networks/convolutional-neural-networks/deep-convolutional-models-case-studies/25-wang-luo-zhong-de-wang-luo-yi-ji-1-1-juan-ji-ff08-network-in-network-and-1-1-convolutions.md)
- [2.5 谷歌 Inception 网络简介（Inception network motivation）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-si-men-ke-juan-ji-shen-jing-wang-luo-convolutional-neural-networks/convolutional-neural-networks/deep-convolutional-models-case-studies/26-gu-ge-inception-wang-luo-jianjie-ff08-inception-network-motivation.md)
- [2.6 Inception 网络（Inception network）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-si-men-ke-juan-ji-shen-jing-wang-luo-convolutional-neural-networks/convolutional-neural-networks/deep-convolutional-models-case-studies/27-inception-wang-luo-ff08-inception-network.md)
- [2.7 迁移学习（Transfer Learning）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-si-men-ke-juan-ji-shen-jing-wang-luo-convolutional-neural-networks/convolutional-neural-networks/deep-convolutional-models-case-studies/29-qian-yi-xue-xi-ff08-transfer-learning.md)
- [2.8 数据扩充（Data augmentation）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-si-men-ke-juan-ji-shen-jing-wang-luo-convolutional-neural-networks/convolutional-neural-networks/deep-convolutional-models-case-studies/210-shu-ju-kuo-chong-ff08-data-augmentation.md)
- [2.9 计算机视觉现状（The state of computer vision）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-si-men-ke-juan-ji-shen-jing-wang-luo-convolutional-neural-networks/convolutional-neural-networks/deep-convolutional-models-case-studies/211-ji-suan-ji-shi-jue-xian-zhuang-ff08-the-state-of-computer-vision.md)
- [Residual Networks](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-si-men-ke-juan-ji-shen-jing-wang-luo-convolutional-neural-networks/convolutional-neural-networks/deep-convolutional-models-case-studies/residual-networks.md)
- [Keras tutorial - the Happy House](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-si-men-ke-juan-ji-shen-jing-wang-luo-convolutional-neural-networks/convolutional-neural-networks/deep-convolutional-models-case-studies/keras-tutorial-happy-house-v2.md)
- [kt\_utils.py](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-si-men-ke-juan-ji-shen-jing-wang-luo-convolutional-neural-networks/convolutional-neural-networks/deep-convolutional-models-case-studies/ktutils-py.md)


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