德塔赛 DataSci 是专注数据科学的播客，每期节目会访谈一位数据科学领域的研究者和工程师。欢迎访问我们的官方网站 detasai.com。
Podcast Overview
德塔赛 DataSci 是专注数据科学的播客，每期节目会访谈一位数据科学领域的研究者和工程师。欢迎访问我们的官方网站 detasai.com。
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Publishing Since
10/22/2017
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Recent Episodes
February 1, 2021
S2E2 - 这是一个看脸的世界
<p>我们在第一眼看到一个人的时候，就会下意识的对这个人产生所谓的第一印象，有些人“看起来”值得信任，有些人“看起来”非常聪明，甚至有些人“看起来”不善交际。为什么我们会仅凭一个人的外表产生这些第一印象？ 宋蔓是毕业于加州大学圣地亚哥分校的博士，她的研究方向是认知科学和机器学习。 这一期节目，她将和我们聊聊她如何用机器学习模型理解第一印象的产生。</p> <p>了解更多，请访问宋蔓的 <a href="https://scholar.google.com/citations?hl=en&user=vqUdgL4AAAAJ" rel="nofollow">Google Scholar 页面</a>。</p> <p>Find out more at <a href="http://detasai.com" rel="nofollow">http://detasai.com</a></p>
January 19, 2021
S2E1 - 用数据科学抗击新冠疫情
<h1>用数据科学抗击新冠疫情</h1> <p>这期节目邀请到了在MIT读运筹学博士的李凌志同学，来和我们聊一下他在新冠疫情时做的传染病数学建模的工作。他们的模型后来被美国疾病控制与预防中心（CDC）和所用， 也被刊登到了纽约时报的头条。我们讨论了用数学建模预测疾病传播的方法和挑战 —— 如何用有限的数据做出有意义的模型？怎么量化模型的好坏？在疫情实时发展的情况下，模型是怎么被优化和改进的？李凌志也和我们分享了一些和医生、医院、决策者合作的故事。</p> <p>想要了解更多，请移步李凌志的公众号文章：</p> <ul> <li>在麻省理工抗击新冠疫情实记 (1): <a href="https://mp.weixin.qq.com/s/brJPYDhl78kaZtKpppQ8qA" rel="nofollow">https://mp.weixin.qq.com/s/brJPYDhl78kaZtKpppQ8qA</a></li> <li>在麻省理工抗击新冠疫情实记 (2): <a href="https://mp.weixin.qq.com/s/feLTugkq_g69ygnm-1jf9w" rel="nofollow">https://mp.weixin.qq.com/s/feLTugkq_g69ygnm-1jf9w</a></li> </ul> <p>Find out more at <a href="http://detasai.com" rel="nofollow">http://detasai.com</a></p>
May 31, 2018
DTS 15 - 语言的表示
<p>用适当的方式表示词语是自然语言处理中一个不可或缺的任务。今天的节目中，我们在 UCSD 的同学唐帅和我们讨论了研究词的向量表示的动机和近几年比较流行的词向量表示算法。我们还提到了学习词的表示常用的数据来源，和学习过程中可能需要考虑到一些困难因素。</p> <ul> <li>本期嘉宾：唐帅（UCSD 博士在读）</li> <li>话题：自然语言处理</li> <li>内容提要： <ul> <li>电子商务系统中的寻找近义词任务</li> <li>词语的表示（representation）和词向量</li> <li>近几年比较成功的词向量的表示算法</li> <li>学习词的表示时常用的数据来源</li> <li>怎么衡量一个词向量表示算法的优劣</li> </ul></li> </ul> <h2>相关链接</h2> <ul> <li><a href="http://shuaitang.github.io/">唐帅同学的个人主页</a></li> <li><a href="http://www.52nlp.cn/?p=8339">斯坦福深度学习与自然语言处理讲义中文翻译</a>，其中第二讲介绍了词向量。</li> </ul>
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