“?”             匹配一个任意字符 “*”                匹配0个或任意多个字符,也就是可以匹配任何内容 “[]”       匹配中括号中任意一个字符.例如:[abc] 代表一定匹配一个字符,或者是a                                      或者是b,或者是c. “[-]”                      匹配中括号中任意一个字符,-代表一个范围.例如:[a-z]代表匹配一个小写字母. “[^]”           …

cg教程下载: http://cgpeers.com http://cgpersia.com http://bbs.ideasr.com/forum-328-1.html http://bbs.ideasr.com/forum-337-1.html  (杂志&期刊) http://www.rr-sc.com/ http://forum.gfxnews.orghttp://rutracker.org www.9iv.com http://www.ccgtv.cn/     资讯 http://ww…

@http://www-cs-faculty.stanford.edu/people/karpathy/cvpr2015papers/ CVPR 2015 papers (in nicer format than this) maintained by @karpathy NEW: This year I also embedded the (1,2-gram) tfidf vectors of all papers with t-sne and placed them in an interf…

论文名和编号 摘要/引言 相关背景和工作 论文方法/模型 实验(数据集)及 分析(一些具体数据) 未来工作/不足 是否有源码 问题 原因 解决思路 优势 基于表示学习的中文分词 编号:1001-9081(2016)10-2794-05 1.为提高中文分词的准确率和未登录词识别率. 1.分词后计算机才能得知中文词语的确切边界,进而理解文本中所包含的语义信息.中文分词是中文自然语言处理的一项基础性工作,是中文信息处理技术发展的技术瓶颈. 1.使用skip-gram模型将文本中的词映射为高维向量空间中…

The major advancements in Deep Learning in 2016 Pablo Tue, Dec 6, 2016 in MACHINE LEARNING DEEP LEARNING GAN Deep Learning has been the core topic in the Machine Learning community the last couple of years and 2016 was not the exception. In this arti…

译自:The Major Advancements in Deep Learning in 2016 建议阅读时间:10分钟 https://tryolabs.com/blog/2016/12/06/majoradvancementsdeeplearning2016/ 在过去的十多年来,深度学习一直是核心话题,2016年也不例外.本文回顾了他们认为可能会推动这个领域发展或已经对这个领域产生巨大贡献的技术.(1)无监督学习有史以来便是科研人员所面临的的主要挑战之一.由于大量产生式模型的提出,201…

Deep Learning in a Nutshell: History and Training This series of blog posts aims to provide an intuitive and gentle introduction to deep learning that does not rely heavily on math or theoretical constructs. The first part in this series provided an…

Final Version 1.1 Date Issued:            13 March 2012 Latest version:         http://www.imsglobal.org/lti/ IPR and Distribution Notices Recipients of this document are requested to submit, with their comments, notification of any relevant patent c…

开源软件包 SENNA 和 word2vec 中都用到了词向量(distributed word representation),当时我就在想,对于我们的中文,是不是也类似地有字向量(distributed character representation)的概念呢? 最近恰好读到复旦大学郑骁庆博士等人的文章 [1]<Deep Learning for Chinese Word Segmentation and POS tagging>.这篇文章利用文 [3] 作者提出的神经网络框架,针对中文…

Machine and Deep Learning with Python Education Tutorials and courses Supervised learning superstitions cheat sheet Introduction to Deep Learning with Python How to implement a neural network How to build and run your first deep learning network Neur…