第一课《神经网络和深度学习》
无
第二课《改善神经网络：超参调整，正则化和优化》
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.
Adam
Kingma, Diederik P., and Jimmy Ba. “Adam: A method for stochastic optimization.” arXiv preprint arXiv:1412.6980 (2014).
第三课《结构化机器学习项目》
无
第四课《卷积神经网络CNN》
LeNet-5
LeCun, Yann, et al. “Gradient-based learning applied to document recognition.” Proceedings of the IEEE 86.11 (1998): 2278-2324.
AlexNet
Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. “Imagenet classification with deep convolutional neural networks.” Advances in neural information processing systems. 2012.
VGG-16
Simonyan, Karen, and Andrew Zisserman. “Very deep convolutional networks for large-scale image recognition.” arXiv preprint arXiv:1409.1556 (2014).
ps: 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.
Network in network
Lin, Min, Qiang Chen, and Shuicheng Yan. “Network in network.” arXiv preprint arXiv:1312.4400 (2013).
Inception Network
Szegedy, Christian, et al. “Going deeper with convolutions.” Proceedings of the IEEE conference on computer vision and pattern recognition. 2015.
OverFeat
Sermanet, Pierre, et al. “Overfeat: Integrated recognition, localization and detection using convolutional networks.” arXiv preprint arXiv:1312.6229 (2013).
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.
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.
Fast R-CNN
Girshick, Ross. “Fast r-cnn.” Proceedings of the IEEE international conference on computer vision. 2015.
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.
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.
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.
Visualizing and understanding convolutional networks
Zeiler, Matthew D., and Rob Fergus. “Visualizing and understanding convolutional networks.” European conference on computer vision. Springer, Cham, 2014.
Neural Style Transfer
Gatys, Leon A., Alexander S. Ecker, and Matthias Bethge. “A neural algorithm of artistic style.” arXiv preprint arXiv:1508.06576 (2015).
第五课《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).
Chung, Junyoung, et al. “Empirical evaluation of gated recurrent neural networks on sequence modeling.” arXiv preprint arXiv:1412.3555 (2014).
LSTM
Hochreiter, Sepp, and Jürgen Schmidhuber. “Long short-term memory.” Neural computation 9.8 (1997): 1735-1780.
t-SNE
Maaten, Laurens van der, and Geoffrey Hinton. “Visualizing data using t-SNE.” Journal of machine learning research 9.Nov (2008): 2579-2605.
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.
A neural probabilistic language model
Bengio, Yoshua, et al. “A neural probabilistic language model.” Journal of machine learning research 3.Feb (2003): 1137-1155.
Word2Vec
Mikolov, Tomas, et al. “Efficient estimation of word representations in vector space.” arXiv preprint arXiv:1301.3781 (2013).
Negative Sampling
Mikolov, Tomas, et al. “Distributed representations of words and phrases and their compositionality.” Advances in neural information processing systems. 2013.
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.
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.
Seq 2 Seq & machine translation
Sutskever, I., O. Vinyals, and Q. V. Le. “Sequence to sequence learning with neural networks.” Advances in NIPS (2014).
Cho, Kyunghyun, et al. “Learning phrase representations using RNN encoder-decoder for statistical machine translation.” arXiv preprint arXiv:1406.1078 (2014).
Image captioning
Mao, Junhua, et al. “Deep captioning with multimodal recurrent neural networks (m-rnn).” arXiv preprint arXiv:1412.6632 (2014).
Vinyals, Oriol, et al. “Show and tell: A neural image caption generator.” Proceedings of the IEEE conference on computer vision and pattern recognition. 2015.
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.
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.
Attention
Bahdanau, Dzmitry, Kyunghyun Cho, and Yoshua Bengio. “Neural machine translation by jointly learning to align and translate.” arXiv preprint arXiv:1409.0473 (2014).
Xu, Kelvin, et al. “Show, attend and tell: Neural image caption generation with visual attention.” International conference on machine learning. 2015.
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.
