【fishexpert个人笔记】学习资料整理

深度学习
机器学习

#21

http://www.52nlp.cn/中英文维基百科语料上的word2vec实验 http://www.cnblogs.com/chenbjin/p/5635853.html


#22

#23

###繁体转换简体工具


#24

#25

http://cs231n.github.io/convolutional-networks/


#26

##文本相似 (BM25算法) https://www.cnblogs.com/DjangoBlog/p/5193550.html


#27

###文本去重 simhash http://grunt1223.iteye.com/blog/964564 ###各种距离总结 http://blog.csdn.net/solomonlangrui/article/details/47454805


#28

####社区发现 http://blog.csdn.net/itplus/article/details/9286905


#29

###svm算法 http://www.ai.mit.edu/courses/6.867-f04/lectures/lecture-7-ho.pdf


#30

###How to Develop a Bidirectional LSTM For Sequence Classification in Python with Keras https://machinelearningmastery.com/develop-bidirectional-lstm-sequence-classification-python-keras/ ###bidirectional_lstm


#31

###word2vec可视化 https://ronxin.github.io/wevi/


#32

####Sklearn 特征工程 https://www.cnblogs.com/jasonfreak/p/5448385.html


#33

###模型评估 https://www.zhihu.com/question/30643044/answer/224360465


#34

#35

请问中文文本中如何抽取关系/观点词? https://www.zhihu.com/question/44633151


#36

理解lstm

http://colah.github.io/posts/2015-08-Understanding-LSTMs/


#37

http://people.csail.mit.edu/regina/6881/


#38

正则表达式


#39

特征工程 Understanding Feature Engineering (Part 1) — Continuous Numeric Data

https://towardsdatascience.com/understanding-feature-engineering-part-1-continuous-numeric-data-da4e47099a7b

Understanding Feature Engineering (Part 2) — Categorical Data

https://towardsdatascience.com/understanding-feature-engineering-part-2-categorical-data-f54324193e63


#40

kaggle竞赛 Carvana Image Masking Challenge–1st Place Winner’s Interview