【Lecture 26】【UC伯克利CS162】【操作系统与系统编程】【Chord, DataCapsules, Quantum Computing】 161 -- 0:41 App Lec18-1【CS 61A】【计算机程序的构造和解释】【SICP】【Announcements】 280 -- 1:28:50 App 【Lecture 24】【UC伯克利CS162】【操作系统与系统编程】【RPC, Distributed File Systems】...
Lecture的时候，来的人不多，也就一半教室的人。授课内容是从SQL开始讲的，后续会涉及到transaction、dat...
Lecture的时候,来的人不多,也就一半教室的人。授课内容是从SQL开始讲的,后续会涉及到transaction、database design以及storage等知识。平时有作业,设有期中和期末,以及3个project,前2个project主要是运用PHP和MySQL搭建网站以及交互检索,最后1个project是运用spark加个feature。 Graphics(CS 174) 这门课程的授课老师是D...
未经作者授权,禁止转载 How linear algebra enters the picture in Spectral Graph Theory. Lecture 13c of a semester-long graduate course on math and CS fundamentals for research in theoretical computer science, taught at Carnegie Mellon University. ...
Lecture的时候，来的人不多，也就一半教室的人。授课内容是从SQL开始讲的，后续会涉及到transaction、...
CS 224N - Natural Language Processing, Stanford University (Lecture videos) CS 124 - From Languages to Information - Stanford University fast.ai Code-First Intro to Natural Language Processing (Github) MOOC - Natural Language Processing - Coursera, University of Michigan Natural Language Processing at...
4. 英文https://www.kosbie.net/cmu/spring-11/15-110/notes/lectures.html作者分享了spring 2011的笔记,按照lecture分类。建议学习路径: 1. 课程理解:先阅读https://mp.weixin.qq.com/s/p8RFIPIM3TDh2CrS0QMwNA了解本节课的相关内容,并制定相关的学习计划。 2. 课程学习:官网最新的笔记是spring2023https:...
especially the United States. I used to think that one of MIT, Standford, UC Berkeley or CMU would get the first place on the list. Unexpectedly, Cornell University is the real champion, and it provides nearly twice as many courses as the second one (although there are rare lecture videos...
CS 224N - Natural Language Processing, Stanford University (Lecture videos) CS 124 - From Languages to Information - Stanford University fast.ai Code-First Intro to Natural Language Processing (Github) MOOC - Natural Language Processing - Coursera, University of Michigan Natural Language Processing at...
guided through a practical application of the ideas of the week. Topics include hashing, dimension reduction and LSH, boosting, linear programming, gradient descent, sampling and estimation, and an introduction to spectral techniques. Prerequisites: CS107 and CS161, or permission from the instructor....