Kind of unrelated, I am wondering does anyone know a good CS lecture note websites? There was a one I saw here few months ago but I forgot the URL.
Learn Quicksort via tap dancing.
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Kind of unrelated, I am wondering does anyone know a good CS lecture note websites? There was a one I saw here few months ago but I forgot the URL.
Learn Quicksort via tap dancing.
I wish these lists were better curated rather than just a dump of links with no commentary.
By no means a textbook, just a barely-edited book-length anthology of primary sources I put together, but let me plug my own: Finite of Sense and Infinite of Thought: A History of Computation, Logic and Algebra https://pron.github.io/computation-logic-algebra
Disclaimer: I helped build this platform as a student
Earlier quoted context omitted.
Nothing worse than courses that require a textbook written by the lecturer decades ago and is now out-of-print, with only one copy in the library that all the students have to fight over.
Yeah there is. Courses that require you to purchase the lecturer's terrible quality printed and photocopied(!) Unpublished "Lecture notes" At least a garbage textbook damages their reputation in their field.
It's also a bit intimidating and overhwelming. There is so much to learn and there is also a danger of just getting through textbooks that cover the same material that you've read before or covering stuff on a surface level without getting any practice with what you've learned. As a data scientist, it feels like anything from mathematics, computer science, statistics, large-scale systems, software engineering in general is within my domain and there is a real danger of spreading oneself a bit too thin and not getting that good at anything.
Their link to "Computer Architecture: A Quantitative Approach (5th edition)" ( https://booksite.elsevier.com/9780123838728/references.php ) is broken. Here's a couple more [1],[2]. I'm also surprised Jeff Erickson's free lecture notes [3] aren't there given 1) its easy to remember domain 2) its incredibly high, practical quality. Practical because I've had interviews that just grab questions from the book, and also b…
Couldn't recommend Jeff Erickson's lecture notes more for algorithms, DP, and the like. I too did rather poorly in the class so you're not alone! In a similar vein, Lawrence Angrave, a systems programming lecturer, has a wonderful crowd-sourced "book" [1] on all things systems programming. It is my go to resource for brushing up on these topics. Lastly, David Forsyth, a statistics/applied ML lecturer has a gold mine…
* Paolo Bory: The Internet Myth https://unglue.it/work/442013/
* The Digital Public Domain: Foundations for an Open Culture https://unglue.it/work/136338/
* Francis daCosta: Rethinking the Internet of Things (APress) https://unglue.it/work/310550/
* Shotts: The Linux Command Line (No Starch Press) https://unglue.it/work/136224/
* Fogel: Producing Open Source Software UT: How to Run a Successful Free Software Project https://unglue.it/work/135870/
* Ryder: Unix as IDE https://unglue.it/work/194054/
More at e.g.