Andrej Karpathy
Andrej Karpathy's Stanford pages host his deep learning projects, image captioning work and PhD thesis, plus other Stanford CS material.
What HN says
HN links the cs.stanford.edu domain for Stanford pages on game theory, CS enrollment history, and research papers, not just Karpathy's own work.
Stats for nerds
284HN people shared it
104linked in comments
2.1Kstory points
Jan 2026last shared
Top posts on HN
- 1Doing well in your courses: Andrej's advice for success (2013)3 people · 684 points · first shared 2018 · 198 comments on HN
- 2Neural Networks That Describe Images17 people · 379 points · first shared 2014 · 60 comments on HN
- 3BLAS-level CPU Performance in 100 Lines of C1 person · 216 points · first shared 2022 · 62 comments on HN
- 4The IT Productivity Paradox (2015)4 people · 91 points · first shared 2020 · 55 comments on HN
- 5NIPS 2014 papers1 person · 90 points · first shared 2014 · 14 comments on HN
- 6ConvNetJS: Deep Learning in your browser18 people · 88 points · first shared 2014 · 1 comments on HN
- 7Stratified Synthesis: Automatically Learning the x86-64 Instruction Set (2016) [pdf]3 people · 74 points · first shared 2022 · 7 comments on HN
- 8A History of Capacity Challenges in Computer Science [pdf]4 people · 72 points · first shared 2016 · 5 comments on HN
- 9Google Glass #ifihadglass Winners1 person · 49 points · first shared 2013 · 48 comments on HN
- 10RISC vs. CISC (2000)3 people · 44 points · first shared 2015 · 35 comments on HN
What HN people wrote when they linked it
- colah3Oct 2016
t-SNE is used very widely in ML, both to analyze input data and to analyze learned representations.
To me, the canonical example of people using t-SNE this way is visualizing word embeddings, like this: http://metaoptimize.s3.amazonaws.com/cw-embeddings-ACL2010/embeddings-mostcommon.EMBEDDING_SIZE=50.png
Also, some lovely examples of using t-SNE to visualize conv net representations: http://cs.stanford.edu/people/karpathy/cnnembed/
I think people have found it very useful as one of the main tools for understanding what deep models are doing (along with optimization-based feature visualization) and to just check that your model is learning. I haven't really heard about people using it to pick input features, but that's probably mostly because I don't really work with anyone doing feature engineering.
- lioetersOct 2022
Planiverse is one of my all-time favorite books. Some tasty morsels:
The Planiverse: Computer Contact with a Two-Dimensional World - Engineering Designs in Planiverse - https://cs.stanford.edu/people/eroberts/courses/soco/projects/2005-06/planiverse/designs.html
---
> Dewdney wrote The Planiverse as..an allegory for his search for a reality deeper than that of scientific enquiry, and his subsequent conversion to Sufiism.
We're the ones living in flatland, a planiverse of limited 3D perspective, who yearn for higher dimensions of experience, a glimpse of the beyond. Unfortunately it's not without risk, as this way madness lies - either as a trap of illusion, or at least as a step toward fundamental truth (if any).
- recursivedoubtsJan 2022
This essay contains the famous "Worse is Better" section, made popular by Jamie Zawinski
https://cs.stanford.edu/people/eroberts/courses/cs181/projects/2010-11/WorseIsBetter/index.php/Worse-is-better.html
Summarized as:
"When tradeoffs are required in a software system, priority should go to implementation simplicity at the expense of completeness or correctness."
See also the wikipedia article:
https://en.wikipedia.org/wiki/Worse_is_better
An interesting aspect of all this is that Gabriel argues with himself in a series of papers under a false name:
https://dreamsongs.com/Files/worse-is-worse.pdf
A great example of someone looking at both sides of an argument.
- jhugoOct 2022
On a whim a few years ago I attended Knuth80[0], a symposium celebrating Knuth's 80th birthday in Piteå, northern Sweden. I traveled there by train from London and spent a few days listening to fascinating lectures and attending the inaugural performance of Fantasia Apocalyptica, performed by Jan Overduin on the wonderful new pipe organ in Piteå. I only had the opportunity to speak briefly with Knuth, but the whole thing (including the location in northern Sweden in winter!) was a wonderful experience, and should there be any further birthday symposiums I would heartily recommend them to anyone!
[0] https://cs.stanford.edu/content/contacting-donald-knuth/news17.html
- tegeekNov 2014
This November is pretty heavy for geeks & Singularity lovers (I'm one of them).
First we saw a man-made machine landing/crash-landing 500 millions KM from Earth.
Then HN discussed this [1] which is IMO one of the biggest machine-learning demo I've ever seen (other than Self-driving cars).
And now this futuristic tech which is aiming to eliminate the screens altogether. (As some of its use cases)
If Magic Leap delivers her promise within next 2-3 years, then we are already ahead than Singularity timeline [2].
1. http://cs.stanford.edu/people/karpathy/deepimagesent/
2. http://en.wikipedia.org/wiki/Predictions_made_by_Ray_Kurzweil#2019 - endtimeJun 2010
As elblanco said, any program worth its salt will have most of those as requirements or electives, even tiny ones like Georgetown. You should be able to check the school's CS department's website and see what their curriculum looks like (example: http://cs.stanford.edu/degrees/undergrad/ProgramSheets.shtml). My email is my profile - if you want me to take a look at any specific school I'd be happy to.
More generally, I think the anti-education sentiment you mentioned is unfounded. I'm colored by my experiences, of course, but I don't think I'd be half the engineer I am today without the education I've had, and I'm not a stupid guy.