RNNLM
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http://www.fit.vutbr.cz/~imikolov/rnnlm/
Introduction
Neural network based language models are nowdays among the most successful techniques for statistical language modeling. They can be easily applied in wide range of tasks, including automatic speech recognition and machine translation, and provide significant improvements over classic backoff n-gram models. The 'rnnlm' toolkit can be used to train, evaluate and use such models.
The goal of this toolkit is to speed up research progress in the language modeling field. First, by providing useful implementation that can demonstrate some of the principles. Second, for the empirical experiments when used in speech recognition and other applications. And finally third, by providing a strong state of the art baseline results, to which future research that aims to "beat state of the art techniques" should compare to.
- RNNLM
- rnnlm源码分析(一)
- rnnlm源码分析(二)
- rnnlm源码分析(三)
- rnnlm源码分析(四)
- rnnlm源码分析(五)
- rnnlm源码分析(六)
- rnnlm源码分析(七)
- rnnlm源码分析(八)
- RNNLM的使用方法
- faster-rnnlm源码剖析
- 语言模型(四) RNNLM
- 深度学习-->NLP-->RNNLM实现
- 自然语言处理(三 RNNLM优化)
- RNNLM——A Toolkit For Language Modeling rnnlm基本功能命令详细介绍
- Recurrent Neural Network Based Language Model(RNNLM)原理及BPTT数学推导
- 关于RNNLM的思考,特别是与HMM,n-gram的区别
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