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+# Neural Network
+
+神经网络入门介绍 [Neural Networks and Deep Learning](http://neuralnetworksanddeeplearning.com/index.html) by Nielsen
+
+LSTM 介绍 [Understanding LSTM Networks](http://colah.github.io/posts/2015-08-Understanding-LSTMs/) by Colah
+
+Transformer:
+- Transformer 介绍 [The Illustrated Transformer](https://jalammar.github.io/illustrated-transformer/) by Jay Alammar
+- Transformer 论文 [Attention Is All You Need]()
+- Transformer Pytorch 实现 [](http://nlp.seas.harvard.edu/2018/04/03/attention.html)
+
+[Dilated CNN](https://arxiv.org/abs/1610.10099)
+
+[RecNN](https://nlp.stanford.edu/~socherr/EMNLP2013_RNTN.pdf)
+
+[Sequence to Sequence Learning with Neural Networks](https://arxiv.org/abs/1409.3215)
+
+Attention:
+- [Neural Machine Translation by Jointly Learning to Align and Translate](https://arxiv.org/abs/1409.0473v7)
+- [Effective Approaches to Attention-based Neural Machine Translation](https://nlp.stanford.edu/pubs/emnlp15_attn.pdf)
+
+
+
+# Natural Language Processing
+
+基于 NLTK 库的自然语言处理实践教程 [The NLTK Book](http://www.nltk.org/book/)
+
+# Natural Language Processing based Neural
+
+基于神经网络的自然语言处理方法的历史演进 [A Review of the Neural History of Natural Language Processing](http://ruder.io/a-review-of-the-recent-history-of-nlp/) by Sebastian Ruder
+
+基于神经网络的自然语言处理常用方法简介 [A Primer on Neural Network Models for Natural Language Processing](http://u.cs.biu.ac.il/~yogo/nnlp.pdf) by Yoav Goldberg
+
+基于深度学习的自然语言处理技术最佳实践 [Deep Learning for NLP Best Practices](http://ruder.io/deep-learning-nlp-best-practices/index.html#attentionhttp://ruder.io/deep-learning-nlp-best-practices/index.html)
+
+
+# Language Model
+
+单词级 n-gram 前向神经网络语言模型 [A Neural Probabilistic Language Model](http://jmlr.org/papers/volume3/bengio03a/bengio03a.pdf) (Bengio et al., 2001; 2003)
+
+字符级 RNN 语言模型介绍 [The Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/)
+
+[好玩的文本生成](https://www.msra.cn/zh-cn/news/features/ruihua-song-20161226)
+
+# Word Embedding
+
+http://ruder.io/word-embeddings-2017/
+
+word2vec paper:
+- [Efficient Estimation of Word Representations in Vector Space](https://arxiv.org/abs/1301.3781)
+- [Distributed Representations of Words and Phrases and their Compositionality](https://arxiv.org/abs/1310.4546)
+
+word2vec tutorial:
+
+- [skip-gram](http://mccormickml.com/2016/04/19/word2vec-tutorial-the-skip-gram-model/) by Chris McCormick
+- [negative sample](http://mccormickml.com/2017/01/11/word2vec-tutorial-part-2-negative-sampling/) by Chris McCormick
+
+[Glove: Global Vectors for Word Representation](https://nlp.stanford.edu/pubs/glove.pdf)
+
+[paragraph vector](https://cs.stanford.edu/~quocle/paragraph_vector.pdf)
+
+[A Convolutional Neural Network for Modelling Sentences](https://www.aclweb.org/anthology/P14-1062)
+
+[Convolutional Neural Networks for Sentence Classification](https://arxiv.org/abs/1408.5882)
+
+[Skip-Thought Vectors](https://arxiv.org/abs/1506.06726)
+
+[A Survey of Cross-lingual Word Embedding Models](https://arxiv.org/abs/1706.04902)
+
+# Pretrained language models
+
+https://thegradient.pub/nlp-imagenet/
+
+[Semi-supervised Sequence Learning](https://arxiv.org/abs/1511.01432)
+
+# Multi-task learning
+
+http://ruder.io/multi-task/
+
+[A Unified Architecture for Natural Language Processing: Deep Neural Networks with Multitask Learning](https://ronan.collobert.com/pub/matos/2008_nlp_icml.pdf)(Collobert and Weston 2008)
+
+[auxiliary task](http://ruder.io/multi-task-learning-nlp/)
+
+# Books&Blog
+
+[深度学习 500 问](https://github.com/scutan90/DeepLearning-500-questions)
+
+[中文自然语言处理相关资料](https://github.com/crownpku/awesome-chinese-nlp)
+
+[中文自然语言处理](https://chinesenlp.xyz/#/zh/) by 滴滴人工智能实验室
+
+http://ruder.io/
+
+http://www.hankcs.com/