算法与计算机科学web.stanford.edu
斯坦福 CS168：现代算法工具箱
CS 168: The Modern Algorithmic Toolbox
2022 — 2024，它一次次回到首页
黑点是上首页（≥ 50 分）的那次投稿，空心点是没冲上去的投稿，点越大分数越高。
导读
斯坦福 CS168《现代算法工具箱》的课程主页，涵盖一致性哈希、流算法、随机化算法、线性代数方法（如 SVD/PCA）等实用且概念简洁的现代算法主题。课程强调选取那些非显然、概念简单且基础有用的思想，而非传统理论课的艰深内容。
为什么经久不衰：它把理论算法课重新设计为面向实践者的工具箱，内容经多年打磨且讲义公开免费，对工程师和学习者始终有参考价值。
HN 评论精选
课程选材标准出色，一致性哈希从被拒稿到支撑 Akamai 的历史极具启发
非常棒。课程追求三重标准：(i) 即使对训练有素的计算机科学家也不显然的思想；(ii) 概念上简单——只有这样的思想你多年后还记得；(iii) 基础性——未来可能对你有用。第一课讲一致性哈希，确实达到了目标。附上算法历史轶事：1997 年该论文发表于 STOC，讽刺的是此前曾被理论会议拒稿，理由是“毫无实用希望”；1998 年 Akamai 成立；1999 年《星球大战》预告片上线导致 apple.com 崩溃，只有 Akamai 缓存能看，由此一战成名；次日乔布斯便致电 Akamai……
FabHK查看英文原文
Very neat. They write: > In this course, we’ll be looking for the following trifecta: (i) ideas that are non-obvious, even to the well-trained computer scientist, so that we’re not wasting your time; (ii) conceptually simple — realistically, these are the only ideas that you might remember a year or more from now, when you’re a start-up founder, senior software engineer, PhD student, etc. (iii) fundamental, meaning that there is some chance that the idea will prove useful to you in the future. In the first lesson, they discuss consistent hashing, and they seem to have achieved their goals. ETA: Must include this anecdote on the history of the algorithm: 1. 1997: The implementation of consistent hashing given in this lecture first appeared in a research paper in STOC (“Symposium on the Theory of Computing”) [...] Ironically, the paper had previously been rejected from a theoretical computer science conference because at least one reviewer felt that “it had no hope of being practical.” 2. 1998: Akamai is founded. 3. March 31, 1999: A trailer for “Star Wars: The Phantom Menace” is released online, with Apple the exclusive official distributor. apple.com goes down almost immediately due to the overwhelming number of download requests. For a good part of the day, the only place to watch (an unauthorized copy?) of the trailer is via Akamai’s Web caches. This put Akamai on the map. 4. April 1, 1999: Steve Jobs, having noticed Akamai’s performance the day before, calls Akamai’s President Paul Sagan to talk. Sagan hangs up on Jobs, thinking it’s an April Fool’s prank by one of the co-founders, Danny Lewin or Tom Leighton.
推荐 Boyd 的 VMLS 教材作为课程线性代数部分的配套阅读
Stephen Boyd 的《VMLS》是这门课向量/矩阵部分（即课程大部分内容）的绝佳背景读物。这本书不同于传统线性代数讲法，聚焦文本分析、图像/信号处理、金融、机器学习等具体应用，避开特征值等晦涩概念。通过建立实用直觉来学习是最好的方式。更进阶的还有 Boyd 等人的《广义低秩模型》，建立了 PCA/SVD 与许多其他矩阵分解方法的联系。
jointpdf查看英文原文
VMLS by Stephen Boyd (also of Stanford) would be great background reading for the vector/matrix stuff (i.e. most of this course): https://news.ycombinator.com/item?id=18678314 I love this book. As opposed to the way that linear algebra is typically introduced, this book focuses on concrete applications (like text analysis, image/signal processing, finance, ML, etc.) and eschews more arcane concepts (like eigen). To me, building practical intuition is the best way to learn* the subject. A more advanced but still accessible manuscript by Boyd et al: Generalized Low Rank Models (establishes connections between PCA/SVD and many other matrix factorization methods, and shows you how to roll your own): https://web.stanford.edu/~boyd/papers/pdf/glrm.pdf
推荐算法可视化教学频道 Reducible 作为补充资源
借这个帖子推荐 YouTube 频道 Reducible，它就像算法版的 3Blue1Brown。
Fr0styMatt88查看英文原文
This seems like a good thread to mention the YouTube channel 'Reducible', it's like 3Blue1Brown for algorithms: https://www.youtube.com/c/Reducible
这门课证明 CS 理论内容对工业界实践确有价值
看起来非常好。有趣的是很多内容也能在理论机器学习或应用数学课程中找到。我原以为会有更多流算法、随机算法等内容。据我经验，当集中不等式出现时，多数 CS 学生会有些发怵。“CS 学的东西对工业界编程没用”是常见抱怨，这门课正是反驳这种狭隘观点的好例子。
noelwelsh查看英文原文
Looks really good. Interesting to me is that a lot of the material you could also find in a theoretical machine learning course or in an applied mathematics course. Before seeing the syllabus I was expecting more on streaming algorithms, randomized algoriths, etc. In my experience most Computer Science students get a bit freaked out when, e.g., concentration inequalities enter the scene. "Nothing I learned in CS has an application to industry programming" is a regular complaint in discussion on education here and elsewhere. This is a good course to point to for people who have this very limited view on CS.
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