来源论文：Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. “Imagenet classification with deep convolutional neural networks.” Advances in neural information processing systems. 2012.
来源论文：Simonyan, Karen, and Andrew Zisserman. “Very deep convolutional networks for large-scale image recognition.” arXiv preprint arXiv:1409.1556 (2014).
论文详解：CNN经典算法VGGNet介绍
代码和预训练资源：VGGNet预训练模型及代码资源
Inception
Inception-v1(GoogLeNet)
来源论文：Szegedy, Christian, et al. “Going deeper with convolutions.” Proceedings of the IEEE conference on computer vision and pattern recognition. 2015.
论文详解：CNN经典算法之Inception V1（GoogLeNet）
代码和预训练资源：GoogLeNet代码资源（Tensorflow）
BN-Inception
来源论文：Szegedy, Christian, et al. “Inception-v4, inception-resnet and the impact of residual connections on learning.” Proceedings of the AAAI conference on artificial intelligence. Vol. 31. No. 1. 2017.
论文详解：CNN经典算法之BN-Inception
代码和预训练资源：BN-Inception代码资源
ResNet
来源论文：Targ, Sasha, Diogo Almeida, and Kevin Lyman. “Resnet in resnet: Generalizing residual architectures.” arXiv preprint arXiv:1603.08029 (2016).
论文详解：ResNet论文详解
代码实现：ResNet代码（超详细注释）+数据集下载地址
R-CNN
R-CNN
来源论文：Girshick, Ross, et al. “Rich feature hierarchies for accurate object detection and semantic segmentation.” Proceedings of the IEEE conference on computer vision and pattern recognition. 2014.
论文详解：R-CNN论文详解
代码实现：R-CNN代码
Fast R-CNN
来源论文：Girshick, Ross. “Fast r-cnn.” Proceedings of the IEEE international conference on computer vision. 2015.
论文详解：Fast R-CNN论文详解
代码实现：Fast R-CNN代码
Faster R-CNN
来源论文：Ren, Shaoqing, et al. “Faster r-cnn: Towards real-time object detection with region proposal networks.” Advances in neural information processing systems 28 (2015).
论文详解：一文读懂Faster RCNN
代码实现：Faster R-CNN代码实现
YOLO
YOLO v1
来源论文：YOLO v1论文
论文详解：YOLO v1详解
代码实现：YOLO v1代码实现
YOLO v2
来源论文：YOLO v2论文
论文详解：YOLO v2详解
代码实现：YOLO v2代码实现
YOLO v3
来源论文：YOLO v3论文
论文详解：YOLO v3详解
代码实现：YOLO v3代码
YOLO v4
来源论文：YOLO v4论文
论文详解：YOLO v4详解
代码实现：YOLO v4代码
RNN的经典算法
RNN
来源论文：Sherstinsky, Alex. “Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network.” Physica D: Nonlinear Phenomena 404 (2020): 132306.
论文详解：通俗易懂的RNN
代码实现：RNN代码
GRU
来源论文：Dey, Rahul, and Fathi M. Salem. “Gate-variants of gated recurrent unit (GRU) neural networks.” 2017 IEEE 60th international midwest symposium on circuits and systems (MWSCAS). IEEE, 2017.
论文详解：GRU(门控循环单元)，易懂
代码实现：GRU代码
LSTM
来源论文：Huang, Zhiheng, Wei Xu, and Kai Yu. “Bidirectional LSTM-CRF models for sequence tagging.” arXiv preprint arXiv:1508.01991 (2015).
论文详解：如何从RNN起步，一步一步通俗理解LSTM
代码实现：LSTM代码实现
Encoder-Decoder
来源论文：Badrinarayanan, Vijay, Alex Kendall, and Roberto Cipolla. “Segnet: A deep convolutional encoder-decoder architecture for image segmentation.” IEEE transactions on pattern analysis and machine intelligence 39.12 (2017): 2481-2495.
论文详解：Encoder-Decoder 模型架构详解
代码实现：Encoder-Decoder代码实现
Attention
来源论文：Knudsen, Eric I. “Fundamental components of attention.” Annu. Rev. Neurosci. 30 (2007): 57-78.
论文详解：注意力机制介绍（attention）
代码实现：Attention代码
Transformer
来源论文：Kitaev, Nikita, Łukasz Kaiser, and Anselm Levskaya. “Reformer: The efficient transformer.” arXiv preprint arXiv:2001.04451 (2020).
