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db.json
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简介\n\n在全民人工智能的时代，自然语言处理已经成为一个重要的研究领域。我们的日常生活遍及着使用自然语言处理技术的应用：每天使用的搜索引擎、搜索引擎中遇到的广告、垃圾邮件归类、自动翻译、语音助手和自动聊天机器人等。近几年，这些应用的蓬勃发展与其背后自然语言技术的突飞猛进有着紧密的联系——而其中最重要的技术突破便是深度学习与自然语言的结合。除了自然语言处理领域，深度学习还帮助研究者们在计算机视觉、图像处理、语音识别和计算机围棋等领域取得了划时代的成果。\n\n\n主讲人：李嫣然\n\n北京大学智能科学专业本科，现为香港理工大学在读博士生。研究方向为自然语言处理中的语义表达和语言生成。曾于自然语言领域的顶级会议上发表多篇论文，合著有《科学的极致——漫谈人工智能》一书。集智俱乐部成员，同时是微信公众号［程序媛的日常］的维护者之一小Ｓ。\n\n地点：网络\n\n时间：7月底至11月初（晚上或周末），每周举办一期，共十期。\n\n## 读书会提纲\nWord Representations\n* Distributional vs. Distributed\n\nWord2Vec \n* paper (model, negative sampling...) \n* code \n* further reading and related issues (polysemy, multilingual...) \n \nRNN \n* neural network basics (back propagation, gradient, regularization, activations) \n* RNN (model, calculation, language modeling) \n* further reading and related issues (gradient vanishing, GRU, LSTM) \n \nCNN \n* convolutional operation basics \n* paper (model, pooling, sentence modeling, variable-length...) \n* further reading and related issues (character-level CNN...) \n \nSentiment Analysis \n* Sentiment Analysis tasks and benchmarks (Stanford TreeBank...) \n* papers (GAN, duyu-tang) \n \nMachine Translation \n* Seq2Seq and encoder-decoder framework \n* papers (attention, code) \n* further reading and related issues (http://www.statmt.org/ued/? n=Public.WeeklyMeeting) \n\n## 相关资料\n\n* [线上读书会资料百度网盘](http://pan.baidu.com/s/1qYN9yji) 密码:j7hu \n* [小S线下读书会视频](http://v.qq.com/x/page/s0311ndiz39.html) \n* [视频slides](https://github.com/swarma/nlp_resource/blob/master/slides/NLP_DL_Yanran.pdf) \n* [读书会笔记](http://mp.weixin.qq.com/s?__biz=MzIzMjQyNzQ5MA==&mid=2247483667&idx=1&sn=658da85489324e125dbd2941cca50bd6&scene=0#wechat_redirect)\n* [github](https://github.com/swarma)\n* [小S个人网站](http://yanran.li/)\n","source":"_posts/swarma-NLP-DL-intro.md","raw":"---\ntitle: 集智NLP&DL黄埔一期\n---\n\n## 简介\n\n在全民人工智能的时代，自然语言处理已经成为一个重要的研究领域。我们的日常生活遍及着使用自然语言处理技术的应用：每天使用的搜索引擎、搜索引擎中遇到的广告、垃圾邮件归类、自动翻译、语音助手和自动聊天机器人等。近几年，这些应用的蓬勃发展与其背后自然语言技术的突飞猛进有着紧密的联系——而其中最重要的技术突破便是深度学习与自然语言的结合。除了自然语言处理领域，深度学习还帮助研究者们在计算机视觉、图像处理、语音识别和计算机围棋等领域取得了划时代的成果。\n\n\n主讲人：李嫣然\n\n北京大学智能科学专业本科，现为香港理工大学在读博士生。研究方向为自然语言处理中的语义表达和语言生成。曾于自然语言领域的顶级会议上发表多篇论文，合著有《科学的极致——漫谈人工智能》一书。集智俱乐部成员，同时是微信公众号［程序媛的日常］的维护者之一小Ｓ。\n\n地点：网络\n\n时间：7月底至11月初（晚上或周末），每周举办一期，共十期。\n\n## 读书会提纲\nWord Representations\n* Distributional vs. Distributed\n\nWord2Vec \n* paper (model, negative sampling...) \n* code \n* further reading and related issues (polysemy, multilingual...) \n \nRNN \n* neural network basics (back propagation, gradient, regularization, activations) \n* RNN (model, calculation, language modeling) \n* further reading and related issues (gradient vanishing, GRU, LSTM) \n \nCNN \n* convolutional operation basics \n* paper (model, pooling, sentence modeling, variable-length...) \n* further reading and related issues (character-level CNN...) \n \nSentiment Analysis \n* Sentiment Analysis tasks and benchmarks (Stanford TreeBank...) \n* papers (GAN, duyu-tang) \n \nMachine Translation \n* Seq2Seq and encoder-decoder framework \n* papers (attention, code) \n* further reading and related issues (http://www.statmt.org/ued/? n=Public.WeeklyMeeting) \n\n## 相关资料\n\n* [线上读书会资料百度网盘](http://pan.baidu.com/s/1qYN9yji) 密码:j7hu \n* [小S线下读书会视频](http://v.qq.com/x/page/s0311ndiz39.html) \n* [视频slides](https://github.com/swarma/nlp_resource/blob/master/slides/NLP_DL_Yanran.pdf) \n* [读书会笔记](http://mp.weixin.qq.com/s?__biz=MzIzMjQyNzQ5MA==&mid=2247483667&idx=1&sn=658da85489324e125dbd2941cca50bd6&scene=0#wechat_redirect)\n* [github](https://github.com/swarma)\n* [小S个人网站](http://yanran.li/)\n","slug":"swarma-NLP-DL-intro","published":1,"date":"2016-07-21T07:35:50.000Z","updated":"2016-07-21T07:35:50.000Z","comments":1,"layout":"post","photos":[],"link":"","_id":"ciqw0juqn0000fs48e3acefqv","content":"<h2 id=\"简介\"><a href=\"#简介\" class=\"headerlink\" title=\"简介\"></a>简介</h2><p>在全民人工智能的时代，自然语言处理已经成为一个重要的研究领域。我们的日常生活遍及着使用自然语言处理技术的应用：每天使用的搜索引擎、搜索引擎中遇到的广告、垃圾邮件归类、自动翻译、语音助手和自动聊天机器人等。近几年，这些应用的蓬勃发展与其背后自然语言技术的突飞猛进有着紧密的联系——而其中最重要的技术突破便是深度学习与自然语言的结合。除了自然语言处理领域，深度学习还帮助研究者们在计算机视觉、图像处理、语音识别和计算机围棋等领域取得了划时代的成果。</p>\n<p>主讲人：李嫣然</p>\n<p>北京大学智能科学专业本科，现为香港理工大学在读博士生。研究方向为自然语言处理中的语义表达和语言生成。曾于自然语言领域的顶级会议上发表多篇论文，合著有《科学的极致——漫谈人工智能》一书。集智俱乐部成员，同时是微信公众号［程序媛的日常］的维护者之一小Ｓ。</p>\n<p>地点：网络</p>\n<p>时间：7月底至11月初（晚上或周末），每周举办一期，共十期。</p>\n<h2 id=\"读书会提纲\"><a href=\"#读书会提纲\" class=\"headerlink\" title=\"读书会提纲\"></a>读书会提纲</h2><p>Word Representations</p>\n<ul>\n<li>Distributional vs. Distributed</li>\n</ul>\n<p>Word2Vec </p>\n<ul>\n<li>paper (model, negative sampling…) </li>\n<li>code </li>\n<li>further reading and related issues (polysemy, multilingual…) </li>\n</ul>\n<p>RNN </p>\n<ul>\n<li>neural network basics (back propagation, gradient, regularization, activations) </li>\n<li>RNN (model, calculation, language modeling) </li>\n<li>further reading and related issues (gradient vanishing, GRU, LSTM) </li>\n</ul>\n<p>CNN </p>\n<ul>\n<li>convolutional operation basics </li>\n<li>paper (model, pooling, sentence modeling, variable-length…) </li>\n<li>further reading and related issues (character-level CNN…) </li>\n</ul>\n<p>Sentiment Analysis </p>\n<ul>\n<li>Sentiment Analysis tasks and benchmarks (Stanford TreeBank…) </li>\n<li>papers (GAN, duyu-tang) </li>\n</ul>\n<p>Machine Translation </p>\n<ul>\n<li>Seq2Seq and encoder-decoder framework </li>\n<li>papers (attention, code) </li>\n<li>further reading and related issues (<a href=\"http://www.statmt.org/ued/?\" target=\"_blank\" rel=\"external\">http://www.statmt.org/ued/?</a> n=Public.WeeklyMeeting) </li>\n</ul>\n<h2 id=\"相关资料\"><a href=\"#相关资料\" class=\"headerlink\" title=\"相关资料\"></a>相关资料</h2><ul>\n<li><a href=\"http://pan.baidu.com/s/1qYN9yji\" target=\"_blank\" rel=\"external\">线上读书会资料百度网盘</a> 密码:j7hu </li>\n<li><a href=\"http://v.qq.com/x/page/s0311ndiz39.html\" target=\"_blank\" rel=\"external\">小S线下读书会视频</a> </li>\n<li><a href=\"https://github.com/swarma/nlp_resource/blob/master/slides/NLP_DL_Yanran.pdf\" target=\"_blank\" rel=\"external\">视频slides</a> </li>\n<li><a href=\"http://mp.weixin.qq.com/s?__biz=MzIzMjQyNzQ5MA==&mid=2247483667&idx=1&sn=658da85489324e125dbd2941cca50bd6&scene=0#wechat_redirect\" target=\"_blank\" rel=\"external\">读书会笔记</a></li>\n<li><a href=\"https://github.com/swarma\" target=\"_blank\" rel=\"external\">github</a></li>\n<li><a href=\"http://yanran.li/\" target=\"_blank\" rel=\"external\">小S个人网站</a></li>\n</ul>\n","excerpt":"","more":"<h2 id=\"简介\"><a href=\"#简介\" class=\"headerlink\" title=\"简介\"></a>简介</h2><p>在全民人工智能的时代，自然语言处理已经成为一个重要的研究领域。我们的日常生活遍及着使用自然语言处理技术的应用：每天使用的搜索引擎、搜索引擎中遇到的广告、垃圾邮件归类、自动翻译、语音助手和自动聊天机器人等。近几年，这些应用的蓬勃发展与其背后自然语言技术的突飞猛进有着紧密的联系——而其中最重要的技术突破便是深度学习与自然语言的结合。除了自然语言处理领域，深度学习还帮助研究者们在计算机视觉、图像处理、语音识别和计算机围棋等领域取得了划时代的成果。</p>\n<p>主讲人：李嫣然</p>\n<p>北京大学智能科学专业本科，现为香港理工大学在读博士生。研究方向为自然语言处理中的语义表达和语言生成。曾于自然语言领域的顶级会议上发表多篇论文，合著有《科学的极致——漫谈人工智能》一书。集智俱乐部成员，同时是微信公众号［程序媛的日常］的维护者之一小Ｓ。</p>\n<p>地点：网络</p>\n<p>时间：7月底至11月初（晚上或周末），每周举办一期，共十期。</p>\n<h2 id=\"读书会提纲\"><a href=\"#读书会提纲\" class=\"headerlink\" title=\"读书会提纲\"></a>读书会提纲</h2><p>Word Representations</p>\n<ul>\n<li>Distributional vs. Distributed</li>\n</ul>\n<p>Word2Vec </p>\n<ul>\n<li>paper (model, negative sampling…) </li>\n<li>code </li>\n<li>further reading and related issues (polysemy, multilingual…) </li>\n</ul>\n<p>RNN </p>\n<ul>\n<li>neural network basics (back propagation, gradient, regularization, activations) </li>\n<li>RNN (model, calculation, language modeling) </li>\n<li>further reading and related issues (gradient vanishing, GRU, LSTM) </li>\n</ul>\n<p>CNN </p>\n<ul>\n<li>convolutional operation basics </li>\n<li>paper (model, pooling, sentence modeling, variable-length…) </li>\n<li>further reading and related issues (character-level CNN…) </li>\n</ul>\n<p>Sentiment Analysis </p>\n<ul>\n<li>Sentiment Analysis tasks and benchmarks (Stanford TreeBank…) </li>\n<li>papers (GAN, duyu-tang) </li>\n</ul>\n<p>Machine Translation </p>\n<ul>\n<li>Seq2Seq and encoder-decoder framework </li>\n<li>papers (attention, code) </li>\n<li>further reading and related issues (<a href=\"http://www.statmt.org/ued/?\">http://www.statmt.org/ued/?</a> n=Public.WeeklyMeeting) </li>\n</ul>\n<h2 id=\"相关资料\"><a href=\"#相关资料\" class=\"headerlink\" title=\"相关资料\"></a>相关资料</h2><ul>\n<li><a href=\"http://pan.baidu.com/s/1qYN9yji\">线上读书会资料百度网盘</a> 密码:j7hu </li>\n<li><a href=\"http://v.qq.com/x/page/s0311ndiz39.html\">小S线下读书会视频</a> </li>\n<li><a href=\"https://github.com/swarma/nlp_resource/blob/master/slides/NLP_DL_Yanran.pdf\">视频slides</a> </li>\n<li><a href=\"http://mp.weixin.qq.com/s?__biz=MzIzMjQyNzQ5MA==&mid=2247483667&idx=1&sn=658da85489324e125dbd2941cca50bd6&scene=0#wechat_redirect\">读书会笔记</a></li>\n<li><a href=\"https://github.com/swarma\">github</a></li>\n<li><a href=\"http://yanran.li/\">小S个人网站</a></li>\n</ul>\n"}],"PostAsset":[],"PostCategory":[],"PostTag":[],"Tag":[]}}