# tirthajyoti/Papers-Literature-ML-DL-RL-AI

Highly cited and useful papers related to machine learning, deep learning, AI, game theory, reinforcement learning

Repository: https://github.com/tirthajyoti/Papers-Literature-ML-DL-RL-AI
Canonical: https://ross.abutalabs.com/products/papers-literature-ml-dl-rl-ai
License: MIT
License Family: permissive
Topics: deep-learning, machine-learning, machine-learning-algorithms, artificial-intelligence, neural-network, paper, learning-theory, reinforcement-learning, statistical-learning, data-mining, statistics, pattern-recognition, data-science, silicon, hardware, game-theory, literature
Last push: 2023-02-18T08:00:16+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3151, "days_push": 1292, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2937, forks 804 (observed 2026-08-28T04:07:30.921153+00:00)

## What it is
A curated collection of highly cited papers, tutorials, and books on machine learning, deep learning, reinforcement learning, AI, game theory, and statistics. It is organized into topic directories such as AI hardware, ML Ops, learning theory, and AI ethics.

## Use cases
- find influential machine learning papers to read
- curated reading list for deep learning
- learn reinforcement learning from seminal papers
- papers on game theory and AI
- statistics and statistical learning literature
- resources on AI fairness bias and ethics

## When to choose
- you want a curated, topic-organized list of foundational ML/DL/RL papers
- you are a student or researcher building background in AI theory
- you want free tutorials and books alongside papers

## When to avoid
- you need runnable code or software tools rather than literature
- you need up-to-date papers, as the collection is infrequently updated
- you want structured courses with exercises instead of a paper list

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: documentation
- domain: machine-learning, deep-learning, artificial-intelligence, tutorials
- platform: cross-platform
- tags: papers, reading-list, reinforcement-learning, game-theory, statistics, awesome-list

## Member repositories
- tirthajyoti/Papers-Literature-ML-DL-RL-AI (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:30.921153+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T07:33:09.226667+00:00, confidence not recorded.
  - readme: https://github.com/tirthajyoti/Papers-Literature-ML-DL-RL-AI (fetched 2026-08-28T04:07:30.921153+00:00, sha 22b06894723f)
- Data as of 2026-08-30T08:39:29.467469+00:00.