“Reading Paper”版本间的差异
来自cslt Wiki
（→tool） |
（→word vector） |
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| （相同用户的4个中间修订版本未显示） | |||
| 第4行： | 第4行： | ||
:* google word2ve[http://code.google.com/p/word2vec/] | :* google word2ve[http://code.google.com/p/word2vec/] | ||
* document vector[http://radimrehurek.com/2014/12/doc2vec-tutorial/?utm_source=rss&utm_medium=rss&utm_campaign=doc2vec-tutorial] | * document vector[http://radimrehurek.com/2014/12/doc2vec-tutorial/?utm_source=rss&utm_medium=rss&utm_campaign=doc2vec-tutorial] | ||
| − | :* genSim | + | :* genSim[https://github.com/piskvorky/gensim/] new function |
| + | * Deep Learning for Java[http://deeplearning4j.org/] | ||
| + | :* word2vec[http://deeplearning4j.org/word2vec.html] | ||
==QA== | ==QA== | ||
| 第22行： | 第24行： | ||
* [[useful tutorial]] | * [[useful tutorial]] | ||
* [[2014-10-20-word2vec|Learning Word Vectors for Sentiment Analysis]] | * [[2014-10-20-word2vec|Learning Word Vectors for Sentiment Analysis]] | ||
| + | * deep learing in nlp | ||
| + | :* distributed representations for compositional semantics [http://arxiv.org/pdf/1411.3146.pdf] | ||
| + | :* Deep Learning for Natural Language Processing and Machine Translation [http://cl.naist.jp/~kevinduh/notes/cwmt14tutorial.pdf] | ||
| + | *Ensemble of Generative and Discriminative Techniques for Sentiment Analysis of Movie Reviews[http://arxiv.org/abs/1412.5335] | ||
| + | :*使用RNN和PV在情感分析效果不错,代码[https://github.com/mesnilgr/iclr15] | ||
==learn report== | ==learn report== | ||
2014年12月29日 (一) 07:58的最后版本
目录
tool
- word2vec tool
- document vector[3]
- genSim[4] new function
- Deep Learning for Java[5]
- word2vec[6]
QA
NN & RNN LM
document classification
word vector
- useful tutorial
- Learning Word Vectors for Sentiment Analysis
- deep learing in nlp
- Ensemble of Generative and Discriminative Techniques for Sentiment Analysis of Movie Reviews[9]
- 使用RNN和PV在情感分析效果不错,代码[10]
learn report
basic tasks of speech processing
learn process
Some Things to remember
- video lectures [11]
- free books [12]
- 推荐系统的tutorial slides [13][14]
- understanding-lbfgs [15]
- ml blog[16][17]
- 公开课[18]
- 机器学习日报[19]
- 包含大量的学习资源
- Advanced Machine Learning[20]
