102 lines
4.1 KiB
Markdown
102 lines
4.1 KiB
Markdown
# Machine Learning
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[Linear Regression](http://ufldl.stanford.edu/tutorial/supervised/LinearRegression/)
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[Logistic Regression](http://ufldl.stanford.edu/tutorial/supervised/LogisticRegression/)
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[Softmax Regression](http://ufldl.stanford.edu/tutorial/supervised/SoftmaxRegression/)
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# Neural Network
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神经网络入门介绍 [Neural Networks and Deep Learning](http://neuralnetworksanddeeplearning.com/index.html) by Nielsen
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LSTM 介绍 [Understanding LSTM Networks](http://colah.github.io/posts/2015-08-Understanding-LSTMs/) by Colah
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Transformer:
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- Transformer 介绍 [The Illustrated Transformer](https://jalammar.github.io/illustrated-transformer/) by Jay Alammar
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- Transformer 论文 [Attention Is All You Need]()
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- Transformer Pytorch 实现 [](http://nlp.seas.harvard.edu/2018/04/03/attention.html)
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[Dilated CNN](https://arxiv.org/abs/1610.10099)
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[RecNN](https://nlp.stanford.edu/~socherr/EMNLP2013_RNTN.pdf)
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[Sequence to Sequence Learning with Neural Networks](https://arxiv.org/abs/1409.3215)
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Attention:
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- [Neural Machine Translation by Jointly Learning to Align and Translate](https://arxiv.org/abs/1409.0473v7)
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- [Effective Approaches to Attention-based Neural Machine Translation](https://nlp.stanford.edu/pubs/emnlp15_attn.pdf)
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# Natural Language Processing
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基于 NLTK 库的自然语言处理实践教程 [The NLTK Book](http://www.nltk.org/book/)
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# Natural Language Processing based Neural
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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
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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
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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)
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# Language Model
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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)
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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/)
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[好玩的文本生成](https://www.msra.cn/zh-cn/news/features/ruihua-song-20161226)
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# Word Embedding
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http://ruder.io/word-embeddings-2017/
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word2vec paper:
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- [Efficient Estimation of Word Representations in Vector Space](https://arxiv.org/abs/1301.3781)
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- [Distributed Representations of Words and Phrases and their Compositionality](https://arxiv.org/abs/1310.4546)
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word2vec tutorial:
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- [skip-gram](http://mccormickml.com/2016/04/19/word2vec-tutorial-the-skip-gram-model/) by Chris McCormick
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- [negative sample](http://mccormickml.com/2017/01/11/word2vec-tutorial-part-2-negative-sampling/) by Chris McCormick
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[Glove: Global Vectors for Word Representation](https://nlp.stanford.edu/pubs/glove.pdf)
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[paragraph vector](https://cs.stanford.edu/~quocle/paragraph_vector.pdf)
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[A Convolutional Neural Network for Modelling Sentences](https://www.aclweb.org/anthology/P14-1062)
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[Convolutional Neural Networks for Sentence Classification](https://arxiv.org/abs/1408.5882)
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[Skip-Thought Vectors](https://arxiv.org/abs/1506.06726)
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[A Survey of Cross-lingual Word Embedding Models](https://arxiv.org/abs/1706.04902)
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# Pretrained language models
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https://thegradient.pub/nlp-imagenet/
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[Semi-supervised Sequence Learning](https://arxiv.org/abs/1511.01432)
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# Multi-task learning
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http://ruder.io/multi-task/
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[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)
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[auxiliary task](http://ruder.io/multi-task-learning-nlp/)
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# Books&Blog
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[深度学习 500 问](https://github.com/scutan90/DeepLearning-500-questions)
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[中文自然语言处理相关资料](https://github.com/crownpku/awesome-chinese-nlp)
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[中文自然语言处理](https://chinesenlp.xyz/#/zh/) by 滴滴人工智能实验室
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http://ruder.io/
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http://www.hankcs.com/
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