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terase梅 的课程评论


terase梅 关注的课程

Analysis of Algorithms (Coursera) 1 个评论 关注

开始时间: 03/04/2016 持续时间: 6 weeks

主页: https://www.coursera.org/course/aofa

简介: This course teaches a calculus that enables precise quantitative predictions of large combinatorial structures. In addition, this course covers generating functions and real asymptotics and then introduces the symbolic method in the context of applications in the analysis of algorithms and basic structures such as permutations, trees, strings, words, and mappings.

Introduction to Recommender Systems (Coursera) 3 个评论 关注

开始时间: 待定 持续时间: Unknown

主页: https://www.coursera.org/course/recsys

简介: This course introduces the concepts, applications, algorithms, programming, and design of recommender systems--software systems that recommend products or information, often based on extensive personalization. Learn how web merchants such as Amazon.com personalize product suggestions and how to apply the same techniques in your own systems!

分布式系统导论 (师徒网) 1 个评论 关注

开始时间: 04/12/2013 持续时间: 15 weeks

主页: http://www.sheetoo.com/app/course/overview?course_id=203

简介: 本课程着重讲述分布式系统的原理与实现,包括分布式环境下编程的基本概念与编程方法,分布式锁,多机备份,一致性问题,可扩展性、安全等相关机制。并且分析了这些原理是如何应用在当前流行的分布式系统中的,如Google File System,MapReduce,Peer to Peer系统,Google BigTable,Amazon Dynamo等。

The Design of Everyday Things (Udacity) 0 个评论 关注

开始时间: 随时 持续时间: 自主

主页: https://www.udacity.com/course/design101

简介: This course will provide you with the knowledge needed to start recognizing the role of design in today’s world, and to start making better design decisions in your own life. In addition to learning basic design concepts such as affordances and signifiers, you will also gain practice in observing and applying design principles.

機器學習基石 (Machine Learning Foundations) (Coursera) 10 个评论 关注

开始时间: 09/08/2015 持续时间: 8 weeks

主页: https://www.coursera.org/course/ntumlone

简介: Machine learning is the study that allows computers to adaptively improve their performance with experience accumulated from the data observed. The course teaches the most fundamental algorithmic, theoretical and practical tools that any user of machine learning needs to know. [機器學習旨在讓電腦能由資料中累積的經驗來自我進步。本課程將介紹各領域中的機器學習使用者都應該知道的基礎演算法、理論及實務工具。]