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josephmisiti/awesome-machine-learning

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cached 2026-03-30T12:55:46.253Z
3mo ago

josephmisiti/awesome-machine-learning

A large, actively maintained curated list of machine learning frameworks, libraries, tools, and learning resources. It is not an application or library itself; the main value is in the categorized reference content across languages and ML subdomains.

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Stars72,099
Forks15,377
Default branchmaster
Last pushed2026-03-15T15:16:19Z
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Prefer upstream for anything current or broadly useful. Choose this fork only if you specifically want the 2016-era snapshot, its legacy additions, or its customized maintenance script and README edits.

Prefer this fork if you want a cleaner, lightly refreshed awesome-list and do not need the newest upstream entries immediately. Prefer upstream if you want the fullest and most current curated ML resource set.

Choose the fork only if you want the added curriculum-oriented organization and are comfortable with an old snapshot. For most adopters, upstream is the better default because it is much more current and actively maintained.

Prefer upstream for almost any practical use. This fork only makes sense if you want a frozen, heavily simplified historical snapshot and are prepared to restore missing resource sections and maintenance tooling yourself.

Choose upstream unless you explicitly need this old snapshot or its small fork-specific scripting. For normal adoption, the fork is too stale and diverged to be a good source of current ML resources.

Choose upstream if you want current ML resource coverage. Choose this fork only if you specifically want an old, trimmed variant or need its added R-package collection workflow; otherwise it is too stale for active adoption.

Prefer upstream unless you specifically need a frozen, custom fork. This fork is stale enough that it is more useful as an archive or starting point for your own curated variant than as a drop-in replacement.

Prefer upstream unless you specifically want a 2016 snapshot or the fork's R-package helper workflow. For most adopters, the staleness and large upstream gap outweigh the fork's small additions.

Choose the upstream unless you specifically need this fork's older snapshot or its R package maintenance workflow. For current ML resource discovery, the fork is too stale to be a good default.