Welcome to ZJU AI Club,The Web is still in construction!
ZJU AI Club Recommended Resources List
📚 Books
🍚 Theory
🔴 Convex Optimization [PDF] ⭐️⭐️⭐️
🔴 Numerical Optimization [PDF] ⭐️⭐️⭐️
🔴 Machine Learning (Chinese) (watermelon book) ⭐️⭐️⭐️⭐️
🔴 Computer Vision : Algorithms and Applications [PDF] ⭐️⭐️⭐️
🔴 Reinforcement Learning:An Introduction [PDF] ⭐️⭐️⭐️⭐️
🔴 解析卷积神经网络 [PDF] ⭐️⭐️⭐️⭐️
🔴 Deep Learning [HTML] [Chinese] ⭐️⭐️⭐️⭐️⭐️
🍔 Practice
🔴 Building Machine Learning Projects with TensorFlow(TensorFlow机器学习实战) ⭐️⭐️⭐️
🔴 Learning OpenCV 3 ⭐️⭐️⭐️
🔴 机器学习实践应用 ⭐️⭐️⭐️
🔴 Python Machine Learning Blueprints(Python机器学习实践指南) ⭐️⭐️⭐️
🔴 Fundamentals and Algorithms of Face Recognition(人脸识别原理及算法) ⭐️⭐️
🔴 Natural Language Processing with Python ⭐️⭐️
🔴 Think Bayes(贝叶斯思维：统计建模的Python学习法) ⭐️⭐️
🔴 The Method of Programming(编程之法) ⭐️
🍜 Framework Documentation
🔴 Tensorflow [Official] [Chinese] ⭐️⭐️⭐️⭐️⭐️
🔴 Keras [Official] [Chinese] ⭐️⭐️⭐️⭐️
🔴 Pytorch [Official] [Chinese] ⭐️⭐️⭐️⭐️⭐️
🔴 Caffe2 [Official] ⭐️⭐️⭐️
🔴 Mxnet [Official] ⭐️⭐️⭐️
🍎 Others
🔴 A Brief History of Artificial Intelligence(人工智能简史) ⭐️
🔴 数学之美 ⭐️⭐️
📺 Videos
🍰 Tutorials
🔴 Stanford Machine Learning(Andrew Ng) [Netease]⭐️⭐️⭐️
🔴 DLAI ML Course(Andrew Ng) [Bilibili]⭐️⭐️
🔴 NTU DL Course(Hungyi Lee) [Youtube] ⭐️⭐️⭐️
🔴 Stanford CS231n:Convolutional Neural Networks for Visual Recognition(Fei-Fei Li) [Official] [Bilibili] [Youtube]⭐️⭐️⭐️⭐️
🔴 Neural Networks for Machine Learning(Geoffrey Hinton) [Coursera] [Bilibili] [Youtube]⭐️⭐️⭐️
🍦 Framework
🔴 Tensorflow(MoFan) [Youtube] [Bilibili] ⭐️⭐️
🔴 Stanford CS 20SI [Tensorflow for DL] [Youtube] [ Bilibili] [Github] ⭐️⭐️⭐️⭐️
🔴 Pytorch(MoFan) [Youtube] [Bilibili] ⭐️⭐️
🔴 Keras(MoFan) [Youtube] [Bilibili] ⭐️⭐️⭐️
🔴 DL with Keras [Youtube] ⭐️⭐️
🔴 MXNet/gluon(Mu Li) [Bilibili] ⭐️⭐⭐️⭐️
📝 Papers
🍅 Early Research
🔴 Hecht-Nielsen R. Theory of the backpropagation neural network[J]. Neural Networks, 1988, 1(Supplement-1): 445-448.（BP Neural Network）[PDF]⭐️⭐️⭐️
🔴 Hinton G E, Osindero S, Teh Y W. A fast learning algorithm for deep belief nets.[J]. Neural Computation, 2006, 18(7):1527-1554.（Milestone of Deep Learning Eve:DBN） [PDF]⭐️⭐️⭐️
🔴 Hinton G E, Salakhutdinov R R. Reducing the dimensionality of data with neural networks.[J]. Science, 2006, 313(5786):504-7.（Milestone, Show the Promise of Deep Learning） [PDF]⭐️⭐️⭐️
🔴 Ng A. Sparse autoencoder[J]. CS294A Lecture notes, 2011, 72(2011): 1-19.（Sparse Autoencoder） [PDF]⭐️⭐️
🔴 Vincent P, Larochelle H, Lajoie I, et al. Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion[J]. Journal of Machine Learning Research, 2010, 11(Dec): 3371-3408.（Stacked Denoising Autoencoders，SAE） [PDF]⭐️⭐️
🍖 Theory
🍑 DL Breakout
🔴 Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems. 2012.（AlexNet） [PDF]⭐️⭐️⭐️
🔴 Simonyan, Karen, and Andrew Zisserman. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014).（VGGNet） [PDF]⭐️⭐️⭐️
🔴 Szegedy, Christian, et al. Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015.（GoogLeNet） [PDF]⭐️⭐️
🔴 Szegedy C, Vanhoucke V, Ioffe S, et al. Rethinking the Inception Architecture for Computer Vision[J]. Computer Science, 2015:2818-2826.（InceptionV3） [PDF]⭐️⭐️
🔴 He, Kaiming, et al. Deep residual learning for image recognition. arXiv preprint arXiv:1512.03385 (2015).（ResNet）[PDF]⭐️⭐️⭐️⭐️⭐️
🔴 Chollet F. Xception: Deep Learning with Depthwise Separable Convolutions[J]. arXiv preprint arXiv:1610.02357, 2016.（Xception）[PDF]⭐️⭐️⭐️
🔴 Huang G, Liu Z, Weinberger K Q, et al. Densely Connected Convolutional Networks[J]. 2016. (DenseNet, 2017 CVPR Best Paper) [PDF]⭐️⭐️⭐️
🔴 Squeeze-and-Excitation Networks. (SeNet, 2017 ImageNet Champion) [PDF]⭐️⭐️⭐️
🔴 Zhang X, Zhou X, Lin M, et al. Shufflenet: An extremely efficient convolutional neural network for mobile devices[J]. arXiv preprint arXiv:1707.01083, 2017.（Shufflenet） [PDF]⭐️⭐️
🔴 Sabour S, Frosst N, Hinton G E. Dynamic routing between capsules[C]//Advances in Neural Information Processing Systems. 2017: 3859-3869.（Hinton, Capsules） [PDF]⭐️⭐️
🍪 Tricks
🔴 Srivastava N, Hinton G E, Krizhevsky A, et al. Dropout: a simple way to prevent neural networks from overfitting[J]. Journal of Machine Learning Research, 2014, 15(1): 1929-1958.（Dropout） [PDF]⭐️⭐️
🔴 Ioffe S, Szegedy C. Batch normalization: Accelerating deep network training by reducing internal covariate shift[J]. arXiv preprint arXiv:1502.03167, 2015.（Batch Normalization） [PDF]⭐️⭐️
🔴 Lin M, Chen Q, Yan S. Network In Network[J]. Computer Science, 2014.（Inspiration of Global Average Pooling） [PDF]⭐️⭐️
🔴 Goyal, Priya, Dollár, Piotr, Girshick, Ross, et al. Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour[J]. 2017. （Facebook Research，Solved Huge Batchsize Network Cause Damage on Performance） [PDF]⭐️⭐️⭐️⭐️
🍡 RNN
🔴 Mikolov T, Karafiát M, Burget L, et al. Recurrent neural network based language model[C]//Interspeech. 2010, 2: 3.（Classical Paper for RNN&language Model） [PDF]⭐️⭐️⭐️
🔴 Kamijo K, Tanigawa T. Stock price pattern recognition-a recurrent neural network approach[C]//Neural Networks, 1990., 1990 IJCNN International Joint Conference on. IEEE, 1990: 215-221.（RNN for Share Price） [PDF]⭐️⭐️
🔴 Hochreiter S, Schmidhuber J. Long short-term memory[J]. Neural computation, 1997, 9(8): 1735-1780.（Mathematics of LSTM） [PDF]⭐️⭐️
🔴 Sak H, Senior A W, Beaufays F. Long short-term memory recurrent neural network architectures for large scale acoustic modeling[C]//Interspeech. 2014: 338-342.（LSTM for Acoustic Recognition） [PDF]⭐️⭐️
🔴 Chung J, Gulcehre C, Cho K H, et al. Empirical evaluation of gated recurrent neural networks on sequence modeling[J]. arXiv preprint arXiv:1412.3555, 2014.（GRUNet） [PDF]⭐️⭐️
🔴 Ling W, Luís T, Marujo L, et al. Finding function in form: Compositional character models for open vocabulary word representation[J]. arXiv preprint arXiv:1508.02096, 2015.（LSTM Applies to Vocabulary Vector） [PDF]⭐️⭐️
🔴 Huang Z, Xu W, Yu K. Bidirectional LSTM-CRF models for sequence tagging[J]. arXiv preprint arXiv:1508.01991, 2015.（Bi-LSTM for Sequence Tagging） [PDF]⭐️⭐️⭐️⭐️
🍟 Attention Model
🔴 Bahdanau D, Cho K, Bengio Y. Neural machine translation by jointly learning to align and translate[J]. arXiv preprint arXiv:1409.0473, 2014.（Attention Model Proposed） [PDF]⭐️⭐️⭐️
🔴 Mnih V, Heess N, Graves A. Recurrent models of visual attention[C]//Advances in neural information processing systems. 2014: 2204-2212.（Attention Model Combine with Vision） [PDF]⭐️⭐️
🔴 Xu K, Ba J, Kiros R, et al. Show, Attend and Tell: Neural Image Caption Generation with Visual Attention[C]//ICML. 2015, 14: 77-81.（Classical Paper for Attention model Applies to Image Caption） [PDF]⭐️⭐️
🔴 Lee C Y, Osindero S. Recursive Recurrent Nets with Attention Modeling for OCR in the Wild[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016: 2231-2239.（Attention Model Applies to OCR） [ PDF]⭐️⭐️
🔴 Gregor K, Danihelka I, Graves A, et al. DRAW: A recurrent neural network for image generation[J]. arXiv preprint arXiv:1502.04623, 2015.（DRAM Combine with Attention Model for Generate Image） [PDF]⭐️⭐️
🍒 GAN
🔴 Goodfellow I, Pouget-Abadie J, Mirza M, et al. Generative adversarial nets[C]//Advances in neural information processing systems. 2014: 2672-2680.（GAN Proposed） [PDF]⭐️⭐️
🔴 Mirza M, Osindero S. Conditional generative adversarial nets[J]. arXiv preprint arXiv:1411.1784, 2014.（CGAN） [PDF]⭐️⭐️
🔴 Radford A, Metz L, Chintala S. Unsupervised representation learning with deep convolutional generative adversarial networks[J]. arXiv preprint arXiv:1511.06434, 2015.（DCGAN） [PDF]⭐️⭐️
🔴 Denton E L, Chintala S, Fergus R. Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks[C]//Advances in neural information processing systems. 2015: 1486-1494.（LAPGAN） [PDF]⭐️⭐️⭐️
🔴 Chen X, Duan Y, Houthooft R, et al. Infogan: Interpretable representation learning by information maximizing generative adversarial nets[C]//Advances in Neural Information Processing Systems. 2016: 2172-2180.（InfoGAN） [ PDF]⭐️⭐️
🔴 Arjovsky M, Chintala S, Bottou L. Wasserstein gan[J]. arXiv preprint arXiv:1701.07875, 2017.（WGAN） [PDF]⭐️⭐️
🔴 Zhu J Y, Park T, Isola P, et al. Unpaired image-to-image translation using cycle-consistent adversarial networks[J]. arXiv preprint arXiv:1703.10593, 2017.（CycleGAN） [PDF]⭐️⭐️
🔴 Yi Z, Zhang H, Gong P T. DualGAN: Unsupervised Dual Learning for Image-to-Image Translation[J]. arXiv preprint arXiv:1704.02510, 2017.（DualGAN） [PDF]⭐️⭐️
🔴 Isola P, Zhu J Y, Zhou T, et al. Image-to-image translation with conditional adversarial networks[J]. arXiv preprint arXiv:1611.07004, 2016.（pix2pix） [PDF]⭐️⭐️
🍇 One/Zero Shot Learning
🔴 Fei-Fei L, Fergus R, Perona P. One-shot learning of object categories[J]. IEEE transactions on pattern analysis and machine intelligence, 2006, 28(4): 594-611.（One Shot Learning） [PDF]⭐️⭐️⭐️
🔴 Larochelle H, Erhan D, Bengio Y. Zero-data learning of new tasks[J]. 2008:646-651.（Zero Shot Learning） [PDF]⭐️⭐️⭐️⭐️
🔴 Palatucci M, Pomerleau D, Hinton G E, et al. Zero-shot learning with semantic output codes[C]//Advances in neural information processing systems. 2009: 1410-1418.（Zero shot learning Classical Application） [PDF]⭐️⭐️⭐️
🍞 Application
🍗 Object Detection
🔴 Szegedy C, Toshev A, Erhan D. Deep neural networks for object detection[C]//Advances in Neural Information Processing Systems. 2013: 2553-2561.（Earliy Stage Object Dection） [PDF]⭐️⭐️
🔴 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.（RCNN） [PDF]⭐️⭐️⭐️⭐️
🔴 He K, Zhang X, Ren S, et al. Spatial pyramid pooling in deep convolutional networks for visual recognition[C]//European Conference on Computer Vision. Springer International Publishing, 2014: 346-361.（SPPNet） [PDF]⭐️⭐️
🔴 Girshick R. Fast R-CNN[C]//Proceedings of the IEEE International Conference on Computer Vision. 2015: 1440-1448.（A Faster R-CNN） [PDF]⭐️⭐️⭐️
🔴 Ren S, He K, Girshick R, et al. Faster R-CNN: Towards real-time object detection with region proposal networks[C]//Advances in neural information processing systems. 2015: 91-99.（A Much Faster R-CNN） [PDF]⭐️⭐️⭐️
🔴 Redmon J, Divvala S, Girshick R, et al. You only look once: Unified, real-time object detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016: 779-788.（Realtime Object Dection : YOLO） [PDF]⭐️⭐️⭐️
🔴 Liu W, Anguelov D, Erhan D, et al. SSD: Single shot multibox detector[C]//European Conference on Computer Vision. Springer International Publishing, 2016: 21-37.（SSD） [PDF]⭐️⭐️⭐️
🔴 Li Y, He K, Sun J. R-fcn: Object detection via region-based fully convolutional networks[C]//Advances in Neural Information Processing Systems. 2016: 379-387.（R-FCN） [PDF]⭐️⭐️⭐️
🔴 Lin T Y, Goyal P, Girshick R, et al. Focal loss for dense object detection[J]. arXiv preprint arXiv:1708.02002, 2017.（Focal loss） [PDF]⭐️⭐️
🍐 Image Segemetation
🔴 Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015: 3431-3440.（Classical Paper of Semantic Segmetation，CVPR2015） [PDF]⭐️⭐️⭐️
🔴 Chen L C, Papandreou G, Kokkinos I, et al. Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs[J]. arXiv preprint arXiv:1606.00915, 2016.（DeepLab） [PDF]⭐️⭐️
🔴 Zhao H, Shi J, Qi X, et al. Pyramid scene parsing network[J]. arXiv preprint arXiv:1612.01105, 2016.（PSPNet） [PDF]⭐️⭐️
🔴 Yu F, Koltun V, Funkhouser T. Dilated residual networks[J]. arXiv preprint arXiv:1705.09914, 2017. [PDF]⭐️⭐️
🔴 He K, Gkioxari G, Dollár P, et al. Mask R-CNN[J]. arXiv preprint arXiv:1703.06870, 2017.（MASK R-CNN） [PDF]⭐️⭐️⭐️⭐️
🔴 Hu R, Dollár P, He K, et al. Learning to Segment Every Thing[J]. arXiv preprint arXiv:1711.10370, 2017.（Mask Rcnn Enhanced Version） [PDF]⭐️⭐️⭐️
🍭 Person Re-ID
🔴 Yi D, Lei Z, Liao S, et al. Deep metric learning for person re-identification[C]//Pattern Recognition (ICPR), 2014 22nd International Conference on. IEEE, 2014: 34-39.（Early Re-ID Based on CNN） [PDF]⭐️⭐️⭐️
🔴 Ding S, Lin L, Wang G, et al. Deep feature learning with relative distance comparison for person re-identification[J]. Pattern Recognition, 2015, 48(10): 2993-3003.（triplet loss） [PDF]⭐️⭐️
🔴 Cheng D, Gong Y, Zhou S, et al. Person re-identification by multi-channel parts-based cnn with improved triplet loss function[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016: 1335-1344.（improved triplet loss） [PDF]⭐️⭐️
🔴 Hermans A, Beyer L, Leibe B. In Defense of the Triplet Loss for Person Re-Identification[J]. arXiv preprint arXiv:1703.07737, 2017.（Triplet Loss With Hard Mining Sample） [PDF]⭐️⭐️⭐️
🔴 Chen W, Chen X, Zhang J, et al. Beyond triplet loss: a deep quadruplet network for person re-identification[J]. arXiv preprint arXiv:1704.01719, 2017.（Quadruplet Network） [PDF]⭐️⭐️
🔴 Zheng Z, Zheng L, Yang Y. Unlabeled samples generated by gan improve the person re-identification baseline in vitro[J]. arXiv preprint arXiv:1701.07717, 2017.（First parper Using GAN for Re-ID） [PDF]⭐️⭐️
🔴 Zhang X, Luo H, Fan X, et al. AlignedReID: Surpassing Human-Level Performance in Person Re-Identification[J]. arXiv preprint arXiv:1711.08184, 2017.（AlignedReid,First Perform Better than Human） [PDF]⭐️⭐️⭐️