ALalexmolas·Feb 10, 2023Submission
Curated Papers on Machine Learning in Production
1 points · Open discussioneugeneyan
applied-ml
AI & Machine Learning2020–2023
eugeneyan / applied-ml
applied-ml
📚 Papers & tech blogs by companies sharing their work on data science & machine learning in production.
7Distinct people
7Mentions
0Explicit recommenders
30.9KGitHub stars
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What it is
📚 Papers & tech blogs by companies sharing their work on data science & machine learning in production.
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.
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Also in these discussions
No other repository is repeatedly linked in the same discussions.
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 month7 dated mentions
Observed mentions: 2020-07-22 13:27:28 UTC to 2023-02-10 15:52:22 UTC.
Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.
View mention counts and date ranges
| Period | Start (UTC) | End (exclusive, UTC) | Mentions | Submissions | Comments |
|---|---|---|---|---|---|
| Jul 2020 | 2020-07-01 00:00:00 UTC | 2020-08-01 00:00:00 UTC | 2 | 2 | 0 |
| Aug 2020 | 2020-08-01 00:00:00 UTC | 2020-09-01 00:00:00 UTC | 0 | 0 | 0 |
| Sep 2020 | 2020-09-01 00:00:00 UTC | 2020-10-01 00:00:00 UTC | 1 | 0 | 1 |
| Oct 2020 | 2020-10-01 00:00:00 UTC | 2020-11-01 00:00:00 UTC | 0 | 0 | 0 |
| Nov 2020 | 2020-11-01 00:00:00 UTC | 2020-12-01 00:00:00 UTC | 0 | 0 | 0 |
| Dec 2020 | 2020-12-01 00:00:00 UTC | 2021-01-01 00:00:00 UTC | 0 | 0 | 0 |
| Jan 2021 | 2021-01-01 00:00:00 UTC | 2021-02-01 00:00:00 UTC | 0 | 0 | 0 |
| Feb 2021 | 2021-02-01 00:00:00 UTC | 2021-03-01 00:00:00 UTC | 0 | 0 | 0 |
| Mar 2021 | 2021-03-01 00:00:00 UTC | 2021-04-01 00:00:00 UTC | 1 | 1 | 0 |
| Apr 2021 | 2021-04-01 00:00:00 UTC | 2021-05-01 00:00:00 UTC | 0 | 0 | 0 |
| May 2021 | 2021-05-01 00:00:00 UTC | 2021-06-01 00:00:00 UTC | 0 | 0 | 0 |
| Jun 2021 | 2021-06-01 00:00:00 UTC | 2021-07-01 00:00:00 UTC | 0 | 0 | 0 |
| Jul 2021 | 2021-07-01 00:00:00 UTC | 2021-08-01 00:00:00 UTC | 0 | 0 | 0 |
| Aug 2021 | 2021-08-01 00:00:00 UTC | 2021-09-01 00:00:00 UTC | 0 | 0 | 0 |
| Sep 2021 | 2021-09-01 00:00:00 UTC | 2021-10-01 00:00:00 UTC | 0 | 0 | 0 |
| Oct 2021 | 2021-10-01 00:00:00 UTC | 2021-11-01 00:00:00 UTC | 0 | 0 | 0 |
| Nov 2021 | 2021-11-01 00:00:00 UTC | 2021-12-01 00:00:00 UTC | 0 | 0 | 0 |
| Dec 2021 | 2021-12-01 00:00:00 UTC | 2022-01-01 00:00:00 UTC | 1 | 1 | 0 |
| Jan 2022 | 2022-01-01 00:00:00 UTC | 2022-02-01 00:00:00 UTC | 0 | 0 | 0 |
| Feb 2022 | 2022-02-01 00:00:00 UTC | 2022-03-01 00:00:00 UTC | 0 | 0 | 0 |
| Mar 2022 | 2022-03-01 00:00:00 UTC | 2022-04-01 00:00:00 UTC | 0 | 0 | 0 |
| Apr 2022 | 2022-04-01 00:00:00 UTC | 2022-05-01 00:00:00 UTC | 0 | 0 | 0 |
| May 2022 | 2022-05-01 00:00:00 UTC | 2022-06-01 00:00:00 UTC | 0 | 0 | 0 |
| Jun 2022 | 2022-06-01 00:00:00 UTC | 2022-07-01 00:00:00 UTC | 0 | 0 | 0 |
| Jul 2022 | 2022-07-01 00:00:00 UTC | 2022-08-01 00:00:00 UTC | 0 | 0 | 0 |
| Aug 2022 | 2022-08-01 00:00:00 UTC | 2022-09-01 00:00:00 UTC | 0 | 0 | 0 |
| Sep 2022 | 2022-09-01 00:00:00 UTC | 2022-10-01 00:00:00 UTC | 0 | 0 | 0 |
| Oct 2022 | 2022-10-01 00:00:00 UTC | 2022-11-01 00:00:00 UTC | 0 | 0 | 0 |
| Nov 2022 | 2022-11-01 00:00:00 UTC | 2022-12-01 00:00:00 UTC | 0 | 0 | 0 |
| Dec 2022 | 2022-12-01 00:00:00 UTC | 2023-01-01 00:00:00 UTC | 0 | 0 | 0 |
| Jan 2023 | 2023-01-01 00:00:00 UTC | 2023-02-01 00:00:00 UTC | 1 | 0 | 1 |
| Feb 2023 | 2023-02-01 00:00:00 UTC | 2023-03-01 00:00:00 UTC | 1 | 1 | 0 |
What Hacker News says
7 sourcesHaugeland is GOFAI/cognitive science, not directly relevant to modern machine learning variety of models unless you are doing reinforcement learning or trees stuff (hey poker/chess/Go bots are pretty cool!). Russel and Norvig are the typical introductory textbooks for those. Marks and Haykins are all severely out of date (they have solid content, but they don't have the same scale of modern deep learning which has many emergent properties).
You are approaching this like an established natural sciences field where old classics = good. This is not true for ML. ML is developing and evolving quickly.
I suggest taking a look at Kevin Murphy's series for the foundational knowledge. Sutton and Barto for reinforcement learning. Mackay's learning algorithms and information theory book is also excellent.
Kochenderfer's ML series is also excellent i
Read the rest
f you like control theory and cybernetics
https://algorithmsbook.com/
https://mitpress.mit.edu/9780262039420/algorithms-for-optimi...
https://mitpress.mit.edu/9780262029254/decision-making-under...
For applied deep learning texts beyond the basics, I recommend picking up some books/review papers on LLMs, Transformers, GANs. For classic NLP, Jurafsky is the go-to.
Seminal deep learning papers:
https://github.com/anubhavshrimal/Machine-Learning-Research-...
Data engineering/science:
https://github.com/eugeneyan/applied-ml
For speculation:
https://en.m.wikipedia.org/wiki/Possible_Minds
Curated papers, articles, & blogs on data science and ML in production
3 points · Open discussionCurated readings of ML in production at big tech
2 points · Open discussionFor applied NLP stuff, I would recommend you to check out this rather comprehensive applied ML resources repository: https://github.com/eugeneyan/applied-ml. You might also find helpful relevant survey papers listed here: https://github.com/eugeneyan/ml-surveys.
In 100 NLP PapersCurated papers and articles on data science and machine learning in production
5 points · Open discussionCollection of machine learning projects applied in production, with results
2 points · Open discussion