This description includes all sources and links for our Kaggle competition and materials related to DL learning, Pytorch and Tensorflow. You can add good references about Deep Learning, and put the links under corresponding section. Remember, don't push articles or datasets directly to the github. For articles, please put the links in README.md file under "Papers" section For datesets, build a script that can download datasets automatically, or use remote datasets on AWS S3.
- Deep Learning RoadMap
DL Learning path - Reinforcement Learning guide
- CS231n slides and notes
- Deep learning book
- UFLDL
- DL Tutorial
- DL Tutorial
- Awesome-deep-vision
- NLP resources
- notes
- Transfer learning
- fast ai DL course
- Deep learning course with notes and lab
- torch_rl : A starting Reinforcement Learning PyTorch Library
- PyTorch可用的VGG模型
- pynotebook about pytorch
- Pytorch Container
- pytorch net
- pytorch squeezenet
- pytorch FlowNets
- Kaggle cat vs Dog 这是很不错的一个参考材料!
- pytorch practice
- Seq2seq pytorch
- pytorch notebooks
- pytorch pre-train model zoo(tensorflow)
- simple pytorch example
- pytorch practice
- pytorch tutorial
- pytorch resource
- neural talk by pytorch
neural talk paper1
neural talk paper2 - pytorch implement caffe resnet
- pytorch openNMT github
- pytorch exercise
- Intro to Tensorflow
- Faster-rcnn
论文链接 - keras toolbox
- Tensorflow for ML
- tensorflow GAN
- ssd
ssd github - RCNN ssd github 还有很多repo,可以google
- Machine learning with tensorflow book
- tensorflow notebook
- tensorflow exercise
- DL experiments tensorflow
- DL resource
- Tensorflow code review
- Multi class text classification with CNN,RNN
- Wasserstein GAN 这篇文章可以重点读一下!
pytorch code
Keras code
讲解Wasserstein GAN的blog
Generative-models
Generate images by Generative model Towards Principled Methods for Training Generative Adversarial Networks - AI core work
- Deep Pose 这篇文章以后可能会用到
- Adversarial Learning for Neural Dialogue Generation 这篇文章很吊,目标是图灵测试
- CVPR-2016
- ICCV 2015(ICCV两年一次,16年未开)
- CVPR 2015
- ICPR 2014
- PAMI(需要登陆或充值,IEEE的库用学校网络或许可以直接查看)
- ICLR 2017
- A Deep Convolutional Auto-Encoder with Pooling - Unpooling Layers in Caffe
- NLP 进展
- Attributing a deep network’s prediction to its input features
- mask RCNN