Mageek

9 ms·

31.
Propulsive landing, derived and coded up(timallanwheeler.com)
2 points by Mageek 5y ago|0 comments
32.
by Mageek 5y ago
This articles frames the Artemis program as an us vs. then contest with Space X. They cannot, and never could, compete with Space X. However, NASA and the U.S. government will certainly benefit a lot if Starship is successful. I’m sure most
33.
by Mageek 5y ago
Wow this is so clean and thorough. Love all of the diagrams
34.
by Mageek 5y ago
Well, universities can’t compete in things like car production or rocket manufacturing but find ways to contribute nonetheless. Researchers have and always will struggle to get resources relative to BigCorp - AI is just joining the party. D
35.
Basic Search Algorithms on Sokoban(timallanwheeler.com)
1 points by Mageek 5y ago|0 comments
36.
by Mageek 5y ago
This is a great list - thank you. I learned a long time ago to not use “you” or similar in code reviews, as it is too easy to associate a critique of code with a critique of the coder. Changing “You should change this” to “This should be ch
37.
by Mageek 5y ago
The author of this post laments how deep learning works, but the suggestion is to solve the problem somehow by only reasoning about how other neurons are firing locally. Isn’t that exactly what deep neural networks already do? A basic feedf
38.
by Mageek 5y ago
Research is not a zero-sum game, it’s about bringing useful knowledge to everyone. If releasing your code aids in understanding what you’re contributing, then by all means please contribute! You don’t even need to spend a ton of time cleani
39.
Hierarchical Temporal Memory of the 1000 Brains Theory(tim.hibal.org)
2 points by Mageek 5y ago|0 comments
40.
by Mageek 5y ago
The Tufte package also allows for typesetting a book with executable Julia code, only in LaTeX: https://github.com/sisl/tufte_algorithms_book It was used to make Algorithms for Optimization and Algorithms for Decision
41.
by Mageek 5y ago
Julia is not object-oriented. If you want to dispatch on the type of action, you include it in the method signature.
42.
by Mageek 5y ago
Love this. Doesn’t Microsoft terminal have to be backwards compatible for a ton of historic features though? I would imagine that those sorts of requirements would essentially hobble it. Breaking backwards compatibility isn’t too intriguing
43.
Algorithms for Optimization Now Open Access(algorithmsbook.com)
5 points by Mageek 6y ago|1 comments
44.
by Mageek 6y ago
Algorithms for Decision Making was recently released as open access. The authors were able to get open access rights for their previous book, Algorithms for Optimization. It is a general reference book for optimization algorithms, with conc
45.
by Mageek 6y ago
Having a partner for your side project helps a lot, in the same way having a gym buddy helps with working out. I generally need my projects to be rewarding unto themselves, that is, the act of working on them should be fulfilling. If the en
46.
by Mageek 6y ago
The cup's context in the talk is that J Blow is discussing how technology is not guaranteed to increase over time, but that we regress as well. The Lycurgus Cup was created in Roman times and showed extremely high levels of technology
47.
by Mageek 6y ago
A print version is planned with MIT Press for release in the fall! PDF will continue to be available for free forever.
48.
by Mageek 6y ago
The authors plan to have the book be free, online, forever!
49.
by Mageek 6y ago
*first two authors (MK and TW) wrote Alg4Opt This new book is in the same format.
50.
by Mageek 6y ago
I work on self-driving flying cars, and I can assure you that the R&D phase includes work that _is_ as glamorous as it sounds, but there are also a ton of i's to dot and t's to cross. The very nature of building a usable produ
51.
by Mageek 6y ago
Yeah but what do you do? Feed it knowledge and it organizes it for you? Feed it queries of some sort?
52.
by Mageek 6y ago
What is the business model?
53.
by Mageek 6y ago
Thank you for sharing your experience! It is really insightful.
54.
Concise Deep Learning Overview [pdf](web.stanford.edu)
2 points by Mageek 6y ago|1 comments
55.
by Mageek 6y ago
This appendix chapter of Algorithms for Decision covers deep learning techniques in a concise but encompassing 17 pages. Algorithms for Decision Making provides a broad introduction to algorithms for decision making under uncertainty and co
56.
by Mageek 6y ago
It would be very interesting to see proper MCTS applied to 2048. Monte Carlo Tree Search performs well on problems with linear reward. 2048 has exponential reward, with subsequent states having much higher tile scores. This nonlinearity ten
57.
by Mageek 7y ago
Nice and concise paper. All that changed was outputting a vector of predicted game values, as we go from having a zero-sum game with two players (which can be summarized with a single value) to a game with n players. Makes sense. The compar
58.
by Mageek 7y ago
Such an amazing video. Really interesting how the primes and the spirals are actually (basically) unrelated, but how it nevertheless leads to an interesting concept on the distribution of primes. Love the visualizations.
59.
by Mageek 7y ago
This was amazing. I’ve never been so captured by an obituary.
60.
by Mageek 8y ago
I am not a lawyer, but I for one would be thrilled to hear of any of this being used for good by anyone, whether for personal or commercial uses. I don't see anything other than the standard MIT Press protection clause behind the cover

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