# hurshd0/must-read-papers-for-ml

Collection of must read papers for Data Science, or Machine Learning / Deep Learning Engineer

Repository: https://github.com/hurshd0/must-read-papers-for-ml
Canonical: https://ross.abutalabs.com/products/must-read-papers-for-ml
License: MIT
License Family: permissive
Topics: deep-learning, machine-learning, data-science, papers, neural-networks, convolutional-networks, recurrent-neural-networks, recommender-system, rnn-lstm, exploratory-data-analysis, data-analysis, generalized-additive-models
Last push: 2023-12-02T01:42:22+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2501, "days_push": 1006, "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 1382, forks 180 (observed 2026-08-28T04:04:34.485017+00:00)

## What it is
A curated collection of must-read papers, reviews, and articles for Data Science, Machine Learning, and Deep Learning practitioners. It organizes foundational and influential papers by topic with suggested reading order.

## Use cases
- find must-read machine learning papers
- curated deep learning reading list
- learn data science fundamentals through papers
- find classic papers on neural networks and CNNs
- reading list for aspiring ML engineers
- find papers on recommender systems and boosting
- essential statistics and EDA papers for data scientists

## When to choose
- you want a curated, ranked list of foundational ML/DL papers
- you are a data science or ML engineer building theoretical foundations
- you want topic-organized paper recommendations with reading order

## When to avoid
- you need tutorials or hands-on code rather than academic papers
- you need up-to-date research on cutting-edge topics
- you want interactive courses or video-based learning

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: documentation
- domain: machine-learning, deep-learning, data-science, tutorials, awesome-lists
- platform: cross-platform
- tags: papers, reading-list, curated-list, neural-networks, recommender-systems

## Member repositories
- hurshd0/must-read-papers-for-ml (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:34.485017+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 2026-08-30T04:40:07.353369+00:00, confidence not recorded.
  - readme: https://github.com/hurshd0/must-read-papers-for-ml (fetched 2026-08-28T04:04:34.485017+00:00, sha e0865ebd4952)
- Data as of 2026-08-30T08:39:29.467469+00:00.