看到了第四课，发现出现论文的频率高了不少，想着把这些论文都粗略的读下，就先把他们列在这里，如有不全烦请告知
第一课：NeuralNetworks & Deep Learning
并没有
第二课：Improving Deep Neural Networks :Hyperparameter tuning, Regularization and Optimization
Dropout
Srivastava, Nitish, et al. “Dropout: a simple way to prevent neural networks from overfitting.” The journal of machine learning research 15.1 (2014): 1929-1958.
论文链接
Adam
Kingma, Diederik P., and Jimmy Ba. “Adam: A method for stochastic optimization.” arXiv preprint arXiv:1412.6980 (2014).
论文链接
第三课：Structuring Machine Learning Projects
也没有
第四课：Convolutional Neural Networks
LeNet-5
LeCun, Yann, et al. “Gradient-based learning applied to document recognition.” Proceedings of the IEEE 86.11 (1998): 2278-2324.
论文链接
AlexNet
Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. “Imagenet classification with deep convolutional neural networks.” Advances in neural information processing systems. 2012.
论文链接
VGG-16
Simonyan, Karen, and Andrew Zisserman. “Very deep convolutional networks for large-scale image recognition.” arXiv preprint arXiv:1409.1556 (2014).
论文链接
Andrew Ng 推荐阅读顺序：
AlexNet > VGG > LeNet
ResNet
He, Kaiming, et al. “Deep residual learning for image recognition.” Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.
论文链接
Network in network
Lin, Min, Qiang Chen, and Shuicheng Yan. “Network in network.” arXiv preprint arXiv:1312.4400 (2013).
论文链接
Inception Network
Szegedy, Christian, et al. “Going deeper with convolutions.” Proceedings of the IEEE conference on computer vision and pattern recognition. 2015.
论文链接
Object Detection
OverFeat
Sermanet, Pierre, et al. “Overfeat: Integrated recognition, localization and detection using convolutional networks.” arXiv preprint arXiv:1312.6229 (2013).
论文链接
YOLO
Redmon, Joseph, et al. “You only look once: Unified, real-time object detection.” Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.
论文链接
R-CNN
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.
论文链接
Fast R-CNN
Girshick, Ross. “Fast r-cnn.” Proceedings of the IEEE international conference on computer vision. 2015.
论文链接
Faster R-CNN
Ren, Shaoqing, et al. “Faster r-cnn: Towards real-time object detection with region proposal networks.” Advances in neural information processing systems. 2015.
论文链接
face recognition
Siamese Network
Taigman, Yaniv, et al. “Deepface: Closing the gap to human-level performance in face verification.” Proceedings of the IEEE conference on computer vision and pattern recognition. 2014.
论文链接
"encoding"
FaceNet
Schroff, Florian, Dmitry Kalenichenko, and James Philbin. “Facenet: A unified embedding for face recognition and clustering.” Proceedings of the IEEE conference on computer vision and pattern recognition. 2015.
论文链接
Neural Style Transfer
Visualizing and understanding convolutional networks
Zeiler, Matthew D., and Rob Fergus. “Visualizing and understanding convolutional networks.” European conference on computer vision. Springer, Cham, 2014.
论文链接
可视化每个节点的激活情况
Neural Style Transfer
Gatys, Leon A., Alexander S. Ecker, and Matthias Bethge. “A neural algorithm of artistic style.” arXiv preprint arXiv:1508.06576 (2015).
论文链接
第五课：Sequence Models
第一周：循环序列模型
GRU
Cho, Kyunghyun, et al. “On the properties of neural machine translation: Encoder-decoder approaches.” arXiv preprint arXiv:1409.1259 (2014).
论文链接
Chung, Junyoung, et al. “Empirical evaluation of gated recurrent neural networks on sequence modeling.” arXiv preprint arXiv:1412.3555 (2014).
论文链接
LSTM
Hochreiter, Sepp, and Jürgen Schmidhuber. “Long short-term memory.” Neural computation 9.8 (1997): 1735-1780.
论文链接
第二周 自然语言处理
t-SNE
Maaten, Laurens van der, and Geoffrey Hinton. “Visualizing data using t-SNE.” Journal of machine learning research 9.Nov (2008): 2579-2605.
论文链接
讲的是如何可视化embedding数据的方法
Analogy reasoning
Mikolov, Tomas, Wen-tau Yih, and Geoffrey Zweig. “Linguistic regularities in continuous space word representations.” Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2013.
论文链接
embedding用于类比推理
Learing word embeddings
A neural probabilistic language model
Bengio, Yoshua, et al. “A neural probabilistic language model.” Journal of machine learning research 3.Feb (2003): 1137-1155.
论文链接
通过滑窗预测下一个词来优化embedding matrix
Word2Vec
Mikolov, Tomas, et al. “Efficient estimation of word representations in vector space.” arXiv preprint arXiv:1301.3781 (2013).
论文链接
Negative Sampling
Mikolov, Tomas, et al. “Distributed representations of words and phrases and their compositionality.” Advances in neural information processing systems. 2013.
论文链接
解决skipgram 中 softmax 计算成本高的问题
GloVe
Pennington, Jeffrey, Richard Socher, and Christopher Manning. “Glove: Global vectors for word representation.” Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP). 2014.
论文链接
Debiasing word embeddings
Bolukbasi, Tolga, et al. “Man is to computer programmer as woman is to homemaker? debiasing word embeddings.” Advances in neural information processing systems. 2016.
论文链接
消除embedding带来的词性的偏差
第三周 序列模型和注意力机制
Seq 2 Seq & machine translation
Sutskever, I., O. Vinyals, and Q. V. Le. “Sequence to sequence learning with neural networks.” Advances in NIPS (2014).
论文链接
Cho, Kyunghyun, et al. “Learning phrase representations using RNN encoder-decoder for statistical machine translation.” arXiv preprint arXiv:1406.1078 (2014).
论文链接
Image captioning
Mao, Junhua, et al. “Deep captioning with multimodal recurrent neural networks (m-rnn).” arXiv preprint arXiv:1412.6632 (2014).
论文链接
Vinyals, Oriol, et al. “Show and tell: A neural image caption generator.” Proceedings of the IEEE conference on computer vision and pattern recognition. 2015.
论文链接
Karpathy, Andrej, and Li Fei-Fei. “Deep visual-semantic alignments for generating image descriptions.” Proceedings of the IEEE conference on computer vision and pattern recognition. 2015.
论文链接
Bleu
Papineni, Kishore, et al. “BLEU: a method for automatic evaluation of machine translation.” Proceedings of the 40th annual meeting on association for computational linguistics. Association for Computational Linguistics, 2002.
论文链接
Attention
Bahdanau, Dzmitry, Kyunghyun Cho, and Yoshua Bengio. “Neural machine translation by jointly learning to align and translate.” arXiv preprint arXiv:1409.0473 (2014).
论文链接
Xu, Kelvin, et al. “Show, attend and tell: Neural image caption generation with visual attention.” International conference on machine learning. 2015.
论文链接
CTC cost
Graves, Alex, et al. “Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks.” Proceedings of the 23rd international conference on Machine learning. ACM, 2006.
论文链接
--------------------------终于看完啦~~，接下来会读其中一部分文章，有精力的话会写个简单的摘要，懒癌，就酱~~~-------------------
