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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 scienti
by FabHK 4y ago
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.
- wodenokoto 4y agoI don’t understand the connection between 1 and 2. What happened to the relationship between Apple and Akamai?
- FabHK 4y agoSorry, I left something out. The authors mention earlier in the lecture that > [The algorithm (Consistent Hashing)] has real applications [and] gave birth to Akamai, which to this day is a major player in the Internet. [...] (Quantitatively, Akamai serves 10-30% of all internet traffic, and has a market cap ≈ $19B.) This makes the rejection notice so funny.
- c7b 4y agoThe rejection notice is funny now, but to researchers whose careers depend on publications, such unfounded rejections are anything but.
- CrimsonRain 4y agoConsistent hashing is needed for cashing/CDN.
- O__________O 4y agoHere’s one explanation of Consistent Hashing by a popular author that just started a systems design YouTube channel: https://m.youtube.com/watch?v=UF9Iqmg94tk https://m.youtube.com/watch?v=UF9Iqmg94tk
- Bilal_io 4y agoI love this channel, I've been watching their videos for a few weeks. The guy has a great ability to break things down and explain complex topics with ease.
- revskill 4y agoI'm often fascinated by the fact that, 80% of mentors failed at simplifying in their explanation of concepts.
- O__________O 4y agoHaven’t looked at his books yet, but they appear to be very popular: https://www.amazon.com/Alex-Xu/e/B08BNMFT7P/ https://www.amazon.com/Alex-Xu/e/B08BNMFT7P/ He also has a cheap online course ($60 a year) that covers everything that’s in the books: https://bytebytego.com/pricing https://bytebytego.com/pricing * note: not affiliated with him, YouTube suggested his videos and I looked into him.
- t_mann 4y ago> (i) ideas that are non-obvious, even to the well-trained computer scientist > In the first lesson, they discuss consistent hashing, and they seem to have achieved their goals. I was really excited when I read your comment here before clicking the link, but having had a look at the rest of the curriculum, I'm slightly underwhelmed. - Generalization ... Empirical risk minimization. - lossy compression - Similarity Search. (Dis)similarity metrics: Jaccard, Euclidean, Lp.... - Regularization. The polynomial embedding and random projection, L2 regularization, and L1 regularization as a computationally tractable surrogate for L0 regularization. - Understanding Principal Component Analysis (PCA). ... The simple geometry of "diagonals in disguise." The power iteration algorithm. - Low-rank matrix approximations. The singular value decomposition (SVD), applications to matrix compression, de-noising, and matrix completion (i.e. recovering missing entries). - Graphs as matrices and the Laplacian of a graph. Interpretations of the largest and smallest eigenvectors/eigenvalues of the Laplacian. Spectral embeddings. Interpretations of the second eigenvalue - Markov Chains, stationary distributions. Markov Chain Monte Carlo (MCMC) - Fourier methods - Compressive sensing - Linear and convex programming. Matrix completion - Differential privacy I was thinking that the vast majority of those topics should be pretty much standard knowledge for a mathematically trained computer scientist (not being an expert in each of them, but knowing the basics of what they do, how they work and where they're applied). What level is this course at?
- v3gas 4y agoGuessing from the number (168), probably second year? So not too basic if that's what you're implying
- t_mann 4y agoOk, for a second-year course that's pretty impressive. I don't know why, I was expecting a postgrad-level course
- shuckles 4y agoThe Stanford CS curriculum only distinguishes on the first digit: 1XX for undergrads and 2XX and above for graduate students. (Undergrads often take one or more graduate courses as they progress.) Most taking this course are advanced 2nd year or 3rd or 4th year students who have taken the introduction to algorithms course. Most/many CS majors never take this class at all.