Comparison
ml-surveys vs awesome-list-of-awesomes
Verdict
Pick ml-surveys if ml-surveys is a collection of detailed review papers summarizing advancements in various AI domains such as deep learning, NLP, CV, graphs, reinforcement learning, and recommendation systems; pick awesome-list-of-awesomes if a directory of curated 'awesome lists' on AI topics like ML, DL, CV.
Markdown twin · ml-surveys alternatives · awesome-list-of-awesomes alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | ml-surveys | awesome-list-of-awesomes |
|---|---|---|
| Maintenance | Dormant (1223d since push) As of 1mo · github_public_v1 | Dormant (991d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Personal account As of 1mo · github_public_v1 | Not a fork · Personal account As of 2w · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | No lockfile (source not queried) As of 1mo · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- ml-surveys
- Survey papers summarizing advances in various AI domains
- awesome-list-of-awesomes
- A curated list of 'Awesome' topic lists related to data lifecycle, ML and DL research
Stars
- ml-surveys
- 2.9k
- awesome-list-of-awesomes
- 345
Forks
- ml-surveys
- 291
- awesome-list-of-awesomes
- 48
Open issues
- ml-surveys
- 2
- awesome-list-of-awesomes
- 1
Language
- ml-surveys
- -
- awesome-list-of-awesomes
- -
Adopt for
- ml-surveys
- ml-surveys is a collection of detailed review papers summarizing advancements in various AI domains such as deep learning, NLP, CV, graphs, reinforcement learning, and recommendation systems.
- awesome-list-of-awesomes
- A directory of curated 'awesome lists' on AI topics like ML, DL, CV.
Persona
- ml-surveys
- -
- awesome-list-of-awesomes
- -
Runtime
- ml-surveys
- -
- awesome-list-of-awesomes
- -
License
- ml-surveys
- MIT
- awesome-list-of-awesomes
- MIT
Last pushed
- ml-surveys
- Mar 17, 2023
- awesome-list-of-awesomes
- Nov 13, 2023
Categories
- ml-surveys
- Computer Vision, Evaluation & Observability, Model Training
- awesome-list-of-awesomes
- Computer Vision, Evaluation & Observability, Model Training
Trust and health
Days since push
- ml-surveys
- 1223d
- awesome-list-of-awesomes
- 991d
Open issues (now)
- ml-surveys
- 2
- awesome-list-of-awesomes
- 1
Full report
- ml-surveys
- Trust report
- awesome-list-of-awesomes
- Trust report
Choose ml-surveys if…
- Tags unique to ml-surveys: embeddings, nlp, recommender-system, reinforcement-learning.
- When you need comprehensive overviews and summaries of the latest research trends in multiple areas within machine learning
- More GitHub stars (2.9k vs 345) - visibility, not fit.
When NOT to use ml-surveys
- If you are seeking detailed technical details, original experiments, or specific algorithm implementations as ml-surveys focuses more on synthesis and summary
- In cases where deep-dive analysis is required into a single niche topic, as ml-surveys provides broad overviews rather than in-depth coverage of individual niches
Choose awesome-list-of-awesomes if…
- Tags unique to awesome-list-of-awesomes: data-science, natural-language-processing.
- When you need diverse resources covering specific areas in data science and machine learning
- More recently updated (last pushed Nov 13, 2023).
When NOT to use awesome-list-of-awesomes
- If you require the latest updates, as not all linked lists are actively maintained
- For deeply curated content on new or niche topics not covered
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (eugeneyan/ml-surveys) · observed Jul 22, 2026
- GitHub forks (eugeneyan/ml-surveys) · observed Jul 22, 2026
- Last push (eugeneyan/ml-surveys) · observed Mar 17, 2023
- License file (MIT) · observed Jul 22, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (Nachimak28/awesome-list-of-awesomes) · observed Aug 1, 2026
- GitHub forks (Nachimak28/awesome-list-of-awesomes) · observed Aug 1, 2026
- Last push (Nachimak28/awesome-list-of-awesomes) · observed Nov 13, 2023
- License file (MIT) · observed Aug 1, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: ml-surveys 2.9k · awesome-list-of-awesomes 345 (synced Jul 22, 2026).
Common questions
- What is the difference between ml-surveys and awesome-list-of-awesomes?
- ml-surveys: Survey papers summarizing advances in various AI domains. awesome-list-of-awesomes: A curated list of 'Awesome' topic lists related to data lifecycle, ML and DL research. See the comparison table for live GitHub stats and shared categories.
- When should I choose ml-surveys over awesome-list-of-awesomes?
- Choose ml-surveys over awesome-list-of-awesomes when Tags unique to ml-surveys: embeddings, nlp, recommender-system, reinforcement-learning; When you need comprehensive overviews and summaries of the latest research trends in multiple areas within machine learning; More GitHub stars (2.9k vs 345) - visibility, not fit.
- When should I choose awesome-list-of-awesomes over ml-surveys?
- Choose awesome-list-of-awesomes over ml-surveys when Tags unique to awesome-list-of-awesomes: data-science, natural-language-processing; When you need diverse resources covering specific areas in data science and machine learning; More recently updated (last pushed Nov 13, 2023).
- When should I avoid ml-surveys?
- If you are seeking detailed technical details, original experiments, or specific algorithm implementations as ml-surveys focuses more on synthesis and summary In cases where deep-dive analysis is required into a single niche topic, as ml-surveys provides broad overviews rather than in-depth coverage of individual niches
- When should I avoid awesome-list-of-awesomes?
- If you require the latest updates, as not all linked lists are actively maintained For deeply curated content on new or niche topics not covered
- Is ml-surveys or awesome-list-of-awesomes more popular on GitHub?
- ml-surveys has more GitHub stars (2,902 vs 345). Stars measure visibility, not whether either tool fits your constraints.
- Are ml-surveys and awesome-list-of-awesomes open source?
- Yes - both are open-source projects on GitHub (ml-surveys: MIT, awesome-list-of-awesomes: MIT).
- Where can I find alternatives to ml-surveys or awesome-list-of-awesomes?
- GraphCanon lists graph-backed alternatives at ml-surveys alternatives and awesome-list-of-awesomes alternatives (ml-surveys markdown twin, awesome-list-of-awesomes markdown twin), ranked by typed relationship edges rather than popularity votes.
- Is there a machine-readable version of this comparison?
- Yes. The markdown twin at this comparison mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
- Which is better maintained, ml-surveys or awesome-list-of-awesomes?
- ml-surveys: Dormant. awesome-list-of-awesomes: Dormant. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.
- Where are the full trust reports for ml-surveys and awesome-list-of-awesomes?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: ml-surveys trust report; awesome-list-of-awesomes trust report.