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owainlewis
awesome-artificial-intelligence
Python2015–2025
owainlewis / awesome-artificial-intelligence

awesome-artificial-intelligence

A curated list of Artificial Intelligence (AI) courses, books, video lectures and papers.

AI & Machine LearningSystems & InfrastructureLearning & ReferenceCreative & MediaScience & EngineeringNetworking
Explore on GitHubRead Hacker News discussions
7Distinct people
7Mentions
0Explicit recommenders
16.8KGitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

A curated list of Artificial Intelligence (AI) courses, books, video lectures and papers.

GitHub

Where it is used

No documented product use has been verified yet.

What people are saying

No recommendation phrase was found in the indexed comments.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

Mentioned in the same Hacker News threads. That is not proof they are substitutes.

This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 3:56:07 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by year7 dated mentions

Observed mentions: 2015-01-27 13:00:55 UTC to 2025-03-03 14:04:40 UTC.

012342015-01-01 00:00:00 UTC to 2016-01-01 00:00:00 UTC (exclusive): 4 mentions; 2 submissions, 2 comments20152016-01-01 00:00:00 UTC to 2017-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2017-01-01 00:00:00 UTC to 2018-01-01 00:00:00 UTC (exclusive): 1 mentions; 1 submissions, 0 comments2018-01-01 00:00:00 UTC to 2019-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2019-01-01 00:00:00 UTC to 2020-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2020-01-01 00:00:00 UTC to 2021-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments20202021-01-01 00:00:00 UTC to 2022-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2022-01-01 00:00:00 UTC to 2023-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-01-01 00:00:00 UTC to 2024-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2024-01-01 00:00:00 UTC to 2025-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-01-01 00:00:00 UTC to 2026-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2025

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
20152015-01-01 00:00:00 UTC2016-01-01 00:00:00 UTC422
20162016-01-01 00:00:00 UTC2017-01-01 00:00:00 UTC000
20172017-01-01 00:00:00 UTC2018-01-01 00:00:00 UTC110
20182018-01-01 00:00:00 UTC2019-01-01 00:00:00 UTC000
20192019-01-01 00:00:00 UTC2020-01-01 00:00:00 UTC000
20202020-01-01 00:00:00 UTC2021-01-01 00:00:00 UTC000
20212021-01-01 00:00:00 UTC2022-01-01 00:00:00 UTC000
20222022-01-01 00:00:00 UTC2023-01-01 00:00:00 UTC000
20232023-01-01 00:00:00 UTC2024-01-01 00:00:00 UTC101
20242024-01-01 00:00:00 UTC2025-01-01 00:00:00 UTC000
20252025-01-01 00:00:00 UTC2026-01-01 00:00:00 UTC101

What Hacker News says

7 sources
mindcrime·Jun 25, 2023
If you're content with the current orthodox view / approach(es) then just browse around Github and find one of those "Awesome X" lists like "Awesome Machine Learning"[1], "Awesome Deep Learning"[2], "Awesome Artificial Intelligence"[3] and so on, and go to town. If you want to go deeper, including taking a step back in time and retracing the path(s) taken, to explore whether or not you might want to choose a different fork... well, that's doable, but it's a slog. I should probably write up a reading list for this approach and put it up on Github. It leads to some weird places though... like right before jumping over to HN and noticing this post, I'd been spending the last hour or so trying to track down two obscure Russian books on neural nets from back in the 1980's / 1990's... where print copies do not appear to be available in the US (
Read the rest
and definitely not translated to English) and the nearest library to me with a copy of the one is the Library of Congress in D.C. Do you need to go down that particular rabbit-hole? Probably not. I'm just particularly interested in revisiting some earlier techniques / theorizing about NN's, that have fallen out of favor and aren't really taught much anymore. [1]: https://github.com/josephmisiti/awesome-machine-learning [2]: https://github.com/ChristosChristofidis/awesome-deep-learnin... [3]: https://github.com/owainlewis/awesome-artificial-intelligenc...
In Ask HN: Self-directed learning path for machine learning in 2023?

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