Implementing deep learning papers in Keras
-
IMAGENET CLASSIFICATION WITH DEEP CONVOLUTIONAL NEURAL NETWORKS (AlexNet) - Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton
https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf -
VISUALIZING AND UNDERSTANDING CONVOLUTIONAL NETWORKS (ZFNet) - Matthew D. Zeiler, Rob Fergus
https://arxiv.org/pdf/1311.2901.pdf -
VERY DEEP CONVOLUTIONAL NETWORKS FOR LARGE-SCALE IMAGE RECOGNITION (VGG19) - Karen Simonyan, Andrew Zisserman
https://arxiv.org/pdf/1409.1556.pdf -
DEEP RESIDUAL LEARNING FOR IMAGE RECOGNITION (Resnet34) - Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
https://arxiv.org/pdf/1512.03385.pdf -
GOING DEEPER WITH CONVOLUTIONS (GoogLeNet) - Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich
https://arxiv.org/pdf/1409.4842.pdf -
RETHINKING THE INCEPTION ARCHITECTURE FOR COMPUTER VISION (InceptionV3) - Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens, Zbigniew Wojna
https://arxiv.org/pdf/1512.00567.pdf -
XCEPTION: DEEP LEARNING WITH DEPTHWISE SEPARABLE CONVOLUTIONS (Xception) - Francois Chollet
https://arxiv.org/pdf/1610.02357.pdf -
MOBILENETS: EFFICIENT CONVOLUTIONAL NEURAL NETWORKS FOR MOBILE VISION APPLICATIONS (MobileNets) - Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, Hartwig Adam
https://arxiv.org/pdf/1704.04861.pdf
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent