图 书 馆
在线服务中的分布式实时贝叶斯学习Distributed, Real-Time Bayesian Learning in Online Service |
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| 课程网址: | http://videolectures.net/acml2013_herbrich_real_time_bayesian_lea... |
| 主讲教师: | Ralf Herbrich |
| 开课单位: | 亚马逊公司 |
| 开课时间: | 2014-03-27 |
| 课程语种: | 英语 |
| 中文简介: |  过去十年中，基于Internet的在线服务（例如搜索，广告，游戏和社交网络）取得了巨大的增长。如今，分析大量用户交互数据非常重要，这是为这些服务建立预测模型以及实时学习这些模型的第一步。在这种情况下，最大的挑战之一是规模：不仅庞大的数据规模需要并行处理，而且还需要分布式模型。在主要在线服务（例如Facebook，Twitter，Amazon或Google）上有数亿活跃用户时，线性或非线性模型中任何特定于用户的功能集所产生的模型的大小都大于可以存储在单个系统中的大小。 > |
| 课程简介: | The last ten years have seen a tremendous growth in Internet-based online services such as search, advertising, gaming and social networking. Today, it is important to analyze large collections of user interaction data as a first step in building predictive models for these services as well as learn these models in real-time. One of the biggest challenges in this setting is scale: not only does the sheer scale of data necessitate parallel processing but it also necessitates distributed models; with hundreds of million active users on major online services such as Facebook, Twitter, Amazon or Google, any user-specific sets of features in a linear or non-linear model yields models of a size bigger than can be stored in a single system. |
| 关 键 词: | 贝叶斯学习; 预测模型 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2020-11-30：zyk |
| 最后编审: | 2020-11-30：zyk |
| 阅读次数: | 38 |