neomatrix369

awesome-ai-ml-dl

Jupyter Notebook2019–2024

neomatrix369 / awesome-ai-ml-dl

awesome-ai-ml-dl

Awesome Artificial Intelligence, Machine Learning and Deep Learning as we learn it. Study notes and a curated list of awesome resources of such topics.

AI & Machine Learning Systems & Infrastructure Learning & Reference Productivity Data & Databases DevOps & Cloud Networking Libraries & SDKs

Explore on GitHub Read Hacker News discussions

2Distinct people

4Mentions

0Explicit recommenders

1.7KGitHub stars

REPOSITORY AT A GLANCE

What to know before exploring

What it is

Awesome Artificial Intelligence, Machine Learning and Deep Learning as we learn it. Study notes and a curated list of awesome resources of such topics.

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

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, 5:04:57 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 year4 dated mentions

Observed mentions: 2019-03-29 14:06:38 UTC to 2024-11-23 01:39:16 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

UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
Period	Start (UTC)	End (exclusive, UTC)	Mentions	Submissions	Comments
2019	2019-01-01 00:00:00 UTC	2020-01-01 00:00:00 UTC	1	1	0
2020	2020-01-01 00:00:00 UTC	2021-01-01 00:00:00 UTC	1	1	0
2021	2021-01-01 00:00:00 UTC	2022-01-01 00:00:00 UTC	0	0	0
2022	2022-01-01 00:00:00 UTC	2023-01-01 00:00:00 UTC	0	0	0
2023	2023-01-01 00:00:00 UTC	2024-01-01 00:00:00 UTC	1	0	1
2024	2024-01-01 00:00:00 UTC	2025-01-01 00:00:00 UTC	1	0	1

What Hacker News says

4 sources

westurner·Nov 23, 2024

From https://news.ycombinator.com/item?id=34619013 : > /? awesome "explainable ai" https://www.google.com/search?q=awesome+%22explainable+ai%22 - (Many other great resources) - https://github.com/neomatrix369/awesome-ai-ml-dl/blob/master... : > Post model-creation analysis, ML interpretation/explainability > /? awesome "explainable ai" "XAI"

In Show HN: Llama 3.2 Interpretability with Sparse Autoencoders

westurner·Feb 1, 2023

Prompt engineering: https://en.wikipedia.org/wiki/Prompt_engineering /? inurl:awesome prompt engineering "llm" site:github.com https://www.google.com/search?q=inurl%3Aawesome+prompt+engin... XAI: Explainable Artificial Intelligence & epistomology https://en.wikipedia.org/wiki/Explainable_artificial_intelli... : > Explainable AI (XAI), or Interpretable AI, or Explainable Machine Learning (XML), [1] is artificial intelligence (AI) in which humans can understand the decisions or predictions made by the AI. [2] It contrasts with the "black box" concept in machine learning where even its designers cannot explain why an AI arrived at a specific decision. [3][4] By refining the mental models of users of AI-powered systems and dismantling their misconceptions, XAI promises to help users perform more effectively. [5] XAI may be an implementation o

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f the social _ right to explanation _. [6] XAI is relevant even if there is no legal right or regulatory requirement. For example, XAI can improve the user experience of a product or service by helping end users trust that the AI is making good decisions. This way the aim of XAI is to explain what has been done, what is done right now, what will be done next and unveil the information the actions are based on. [7] These characteristics make it possible (i) to confirm existing knowledge (ii) to challenge existing knowledge and (iii) to generate new assumptions. [8] Right to explanation: https://en.wikipedia.org/wiki/Right_to_explanation (Edit; all human) /? awesome "explainable ai" https://www.google.com/search?q=awesome+%22explainable+ai%22 - (Many other great resources) - https://github.com/neomatrix369/awesome-ai-ml-dl/blob/master... : > Post model-creation analysis, ML interpretation/explainability /? awesome "explainable ai" "XAI" https://www.google.com/search?q=awesome+%22explainable+ai%22...

In ChatGPT is a bullshit generator but it can still be amazingly useful

neomatrix369·Jan 31, 2020Submission

Ask HN: Where can I find examples and resources on how to vectorize in Python?

Mainly the lambda like or .apply() like code styles? Where can I find examples and resources on how to vectorize code in Python (for fast Pandas and Numpy execution), for e.g. vectorize code like this: def some_function(): return group_series.apply(lambda x: x.astype(some_type).rolling(some_number).sum().shift(some_number)) or even something like this: def some_function(): return some_grouped_data_series.apply( lambda x: x.some_function(param1=value1, param2=value2).mean().shift(some_number) ) Mainly the lambda like or .apply() like code styles Any suggestions I should try to vectorize the above, I tried a few things but it complains or I get undesired results. My python performance shares can be found here: https://github.com/neomatrix369/awesome-ai-ml-dl/blob/master/Programming-in-Python…, plea

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se feel free to share yours via a PR.

3 points · Open discussion

neomatrix369·Mar 29, 2019Submission

Grakn and Graql on GraalVM (In a Docker Container)

7 points · Open discussion