1.如何開(kāi)啟深度學(xué)習(xí)之旅？這三大類(lèi)125篇論文為你導(dǎo)航（附資源下載）
https://www.jiqizhixin.com/articles/375cf437-4690-4308-b538-afe9d8cb2b89
2.卷積神經(jīng)網(wǎng)絡(luò)的推導(dǎo)和實(shí)現(xiàn)
http://cogprints.org/5869/1/cnn_tutorial.pdf
3.LeNet論文
http://vision.stanford.edu/cs598_spring07/papers/Lecun98.pdf
4.AlexNet論文
http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf
5.VGGNet論文
http://www.robots.ox.ac.uk/~vgg/research/very_deep/
6.NIN(Network in Network)論文
https://arxiv.org/abs/1312.4400
7.GoogLeNet論文
https://arxiv.org/abs/1409.4842
8.ResNet(Residual Network，殘差網(wǎng)絡(luò))
https://arxiv.org/abs/1512.03385
9.RL(reinforcement learning，強(qiáng)化學(xué)習(xí))
10.深度森林
https://arxiv.org/abs/1702.08835
11.藝術(shù)風(fēng)格的神經(jīng)網(wǎng)絡(luò)算法（A Nerual Algorithm of Artistic Style)
https://arxiv.org/pdf/1508.06576v2.pdf
12.作曲風(fēng)格的深度學(xué)習(xí)創(chuàng)作，代碼實(shí)現(xiàn)
https://github.com/tensorflow/magenta
