A guide about deep-learning
教材
code:https://github.com/mnielsen/neural-networks-and-deep-learning
本书官方主页：https://github.com/d2l-ai/d2l-zh
官方主页：https://github.com/nndl/nndl.github.io
book：https://github.com/cnbeining/deep-learning-with-python-cn
note：https://github.com/fchollet/deep-learning-with-python-notebooks
- 深度学习500问：https://github.com/scutan90/DeepLearning-500-questions
- 算法/深度学习/NLP面试笔记：https://github.com/imhuay/Algorithm_Interview_Notes-Chinese
- Awesome-Deep-Learning-for-Chinese ：https://github.com/bo-xiong/Awesome-Deep-Learning-for-Chinese
- awesome-deep-learning：https://github.com/ChristosChristofidis/awesome-deep-learning
- Top-Deep-Learning：https://github.com/mbadry1/Top-Deep-Learning
- 中文NLP资料：https://github.com/crownpku/Awesome-Chinese-NLP
- faceai：https://github.com/adsk47/faceai
- kaggle项目：https://github.com/adsk47/kaggle
- face_recognition:https://github.com/ageitgey/face_recognition/blob/master/README_Simplified_Chinese.md
- PyTorch中文文档：https://pytorch-cn.readthedocs.io/zh/latest/
- Pytorch中文网：https://ptorch.com
- 官方源码：https://github.com/pytorch/pytorch
- pytorch-book：https://github.com/adsk47/pytorch-book
- PyTorch-Tutorial：https://github.com/adsk47/PyTorch-Tutorial（英语）
- examples：https://github.com/adsk47/examples
- Awesome-pytorch-list：https://github.com/bharathgs/Awesome-pytorch-list
- mxnet：http://mxnet.incubator.apache.org/tutorials/index.html
- numpy：https://www.numpy.org.cn/
- pandas：https://apachecn.github.io/pandas-doc-zh/
- Keras：https://keras-cn.readthedocs.io/en/latest/
- TensorFlow：http://cwiki.apachecn.org/pages/viewpage.action?pageId=10030122
- Scipy：https://scipy.org/getting-started.html（英文）
- theano：http://deeplearning.net/software/theano_versions/dev/index.html（英文）
- OpenCV：http://www.opencv.org.cn/opencvdoc/2.3.2/html/doc/tutorials/tutorials.html
- pwc（Papers with code）：https://github.com/zziz/pwc
- Deep-Learning-Papers-Reading-Roadmap：https://github.com/floodsung/Deep-Learning-Papers-Reading-Roadmap
- awesome-deep-learning-papers：https://github.com/terryum/awesome-deep-learning-papers
- Papers with code：https://paperswithcode.com(受欢迎的论文及其实现）
- Semantic Scholar:https://www.semanticscholar.org（论文及作者趋势，只能搜索）
- Arxiv Sanity Preserver:http://arxiv-sanity.com（最新的论文）