Ask HN: Older textbooks/papers you consider classics still worth studying today?
11–20 of 87 posts
Re: Ask HN: Older textbooks/papers you consider classics still worth studying today?
#12I loved this book as a teenager and recently got reacquainted with this after years by the almighty AvE on youtube when he took apart this an old Helicopter Radio/Telephone here: https://www.youtube.com/watch?v=6eoBj5W7Vdc
Re: Ask HN: Older textbooks/papers you consider classics still worth studying today?
#13Re: Ask HN: Older textbooks/papers you consider classics still worth studying today?
#14Re: Ask HN: Older textbooks/papers you consider classics still worth studying today?
#15Re: Ask HN: Older textbooks/papers you consider classics still worth studying today?
#16What Is Mathematics? by Richard Courant and Herbert Robbins published in 1941. One of the most beginner friendly yet rigorous books out there for a survey of many areas in mathematics.
You can find hundreds of gems here from erstwhile Soviet Union - https://mirtitles.org/
Re: Ask HN: Older textbooks/papers you consider classics still worth studying today?
#17Reading the book is the most beautiful and simple way that a person can really understand what a computer and come to the realization that it is not black magic.
https://www.amazon.com/Code-Language-Computer-Developer-Prac...
Re: Ask HN: Older textbooks/papers you consider classics still worth studying today?
#18Re: Ask HN: Older textbooks/papers you consider classics still worth studying today?
#19Claude Shannon's original 1948 paper "A Mathematical Theory of Communication" launched the entire field of information theory. It's 50 pages, highly readable, and pedagogical. The source of its magic is that Shannon introduces and concretely grounds an essentially new ontological concept of vast applicability. And it has 100,000 citations . http://math.harvard.edu/~ctm/home/text/others/shannon/entrop...
That common interview question about query autocomplete/sentence completion? Shannon solved it and demonstrates it in this paper, almost a decade before FORTRAN existed. New grads still struggle with that problem. PhD's still struggle with that problem.
Pretty much every machine learning classifier is using a loss function described in that paper.