{"adoption": {"forks": 346, "observed_at": "2026-08-28T04:04:48.785455+00:00", "stars": 1467}, "canonical_url": "https://ross.abutalabs.com/products/optml_course", "card": {"archived": false, "artifact_type": "learning-resource", "description": "EPFL Course - Optimization for Machine Learning - CS-439", "domain": ["machine-learning", "tutorials", "education", "mathematics"], "enriched": true, "function": ["machine-learning", "math", "developer-tools"], "health_score": 75, "homepage": null, "language": "Jupyter Notebook", "license": null, "license_family": "other", "maturity": "active", "member_repos": ["epfml/OptML_course"], "name": "epfml/OptML_course", "platform": ["python", "cross-platform"], "pushed_at": "2026-07-14T07:57:35+00:00", "repo": "epfml/OptML_course", "stars": 1467, "tags": ["optimization", "course-materials", "convex-optimization", "gradient-descent", "epfl", "jupyter-notebooks", "lecture-slides", "federated-learning", "algorithms"], "topics": [], "urls": [], "use_cases": ["learn optimization methods for machine learning", "study gradient descent and stochastic gradient descent theory", "find exercises on convex optimization and duality", "self-study a university course on optimization for data science", "prepare for ML interviews covering optimization algorithms", "learn about proximal and subgradient methods", "understand non-convex optimization and neural network training"], "what_it_is": "EPFL's CS-439 'Optimization for Machine Learning' course materials, including lecture slides and lab exercises in Jupyter notebooks. It covers convexity, gradient methods, proximal and stochastic algorithms, duality, second-order methods, and advanced topics like distributed optimization and federated learning.", "when_to_avoid": ["you need production optimization software or solver libraries", "you want a quick reference rather than a full course", "you need a formally licensed, redistributable resource (no license is specified)"], "when_to_choose": ["you want structured, university-grade course material on ML optimization", "you prefer learning with slides plus hands-on notebook exercises", "you need coverage of both convex and non-convex optimization theory"]}, "data_as_of": "2026-08-30T08:39:29.467469+00:00", "members": [{"path": "/products/optml_course", "repo": "epfml/OptML_course", "role": "main", "score": 74}], "provenance": {"archived": {"kind": "observed", "observed_at": "2026-08-28T04:04:48.785455+00:00", "source": "github"}, "artifact_type": {"confidence": null, "enriched_at": "2026-08-30T04:34:59.432186+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "416da08f569d583eb54f4445950e31c937e8c49478d964ca9ecc78ad8138b777", "fetched_at": "2026-08-28T04:04:48.785455+00:00", "kind": "readme", "missing": false, "url": "https://github.com/epfml/OptML_course"}], "taxonomy_version": 1}, "description": {"kind": "observed", "observed_at": "2026-08-28T04:04:48.785455+00:00", "source": "github"}, "domain": {"confidence": null, "enriched_at": "2026-08-30T04:34:59.432186+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "416da08f569d583eb54f4445950e31c937e8c49478d964ca9ecc78ad8138b777", "fetched_at": "2026-08-28T04:04:48.785455+00:00", "kind": "readme", "missing": false, "url": "https://github.com/epfml/OptML_course"}], "taxonomy_version": 1}, "enriched": {"inputs": [], "kind": "computed", "method": "enrichment_status"}, "function": {"confidence": null, "enriched_at": "2026-08-30T04:34:59.432186+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "416da08f569d583eb54f4445950e31c937e8c49478d964ca9ecc78ad8138b777", "fetched_at": "2026-08-28T04:04:48.785455+00:00", "kind": "readme", "missing": false, "url": "https://github.com/epfml/OptML_course"}], "taxonomy_version": 1}, "health_score": {"inputs": ["days_since_push", "days_since_release", "archived"], "kind": "computed", "method": "health_v1"}, "homepage": {"kind": "observed", "observed_at": "2026-08-28T04:04:48.785455+00:00", "source": "github"}, "language": {"kind": "observed", "observed_at": "2026-08-28T04:04:48.785455+00:00", "source": "github"}, "license": {"kind": "observed", "observed_at": "2026-08-28T04:04:48.785455+00:00", "source": "github"}, "license_family": {"inputs": ["license"], "kind": "computed", "method": "license_family"}, "maturity": {"confidence": null, "enriched_at": "2026-08-30T04:34:59.432186+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "416da08f569d583eb54f4445950e31c937e8c49478d964ca9ecc78ad8138b777", "fetched_at": "2026-08-28T04:04:48.785455+00:00", "kind": "readme", "missing": false, "url": "https://github.com/epfml/OptML_course"}], "taxonomy_version": 1}, "member_repos": {"kind": "observed", "observed_at": "2026-08-28T04:04:48.785455+00:00", "source": "github"}, "name": {"kind": "observed", "observed_at": "2026-08-28T04:04:48.785455+00:00", "source": "github"}, "platform": {"confidence": null, "enriched_at": "2026-08-30T04:34:59.432186+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "416da08f569d583eb54f4445950e31c937e8c49478d964ca9ecc78ad8138b777", "fetched_at": "2026-08-28T04:04:48.785455+00:00", "kind": "readme", "missing": false, "url": "https://github.com/epfml/OptML_course"}], "taxonomy_version": 1}, "pushed_at": {"kind": "observed", "observed_at": "2026-08-28T04:04:48.785455+00:00", "source": "github"}, "repo": {"kind": "observed", "observed_at": "2026-08-28T04:04:48.785455+00:00", "source": "github"}, "stars": {"kind": "observed", "observed_at": "2026-08-28T04:04:48.785455+00:00", "source": "github"}, "tags": {"confidence": null, "enriched_at": "2026-08-30T04:34:59.432186+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "416da08f569d583eb54f4445950e31c937e8c49478d964ca9ecc78ad8138b777", "fetched_at": "2026-08-28T04:04:48.785455+00:00", "kind": "readme", "missing": false, "url": "https://github.com/epfml/OptML_course"}], "taxonomy_version": 1}, "topics": {"kind": "observed", "observed_at": "2026-08-28T04:04:48.785455+00:00", "source": "github"}, "urls": {"kind": "observed", "observed_at": "2026-08-28T04:04:48.785455+00:00", "source": "github"}, "use_cases": {"confidence": null, "enriched_at": "2026-08-30T04:34:59.432186+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "416da08f569d583eb54f4445950e31c937e8c49478d964ca9ecc78ad8138b777", "fetched_at": "2026-08-28T04:04:48.785455+00:00", "kind": "readme", "missing": false, "url": "https://github.com/epfml/OptML_course"}], "taxonomy_version": 1}, "what_it_is": {"confidence": null, "enriched_at": "2026-08-30T04:34:59.432186+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "416da08f569d583eb54f4445950e31c937e8c49478d964ca9ecc78ad8138b777", "fetched_at": "2026-08-28T04:04:48.785455+00:00", "kind": "readme", "missing": false, "url": "https://github.com/epfml/OptML_course"}], "taxonomy_version": 1}, "when_to_avoid": {"confidence": null, "enriched_at": "2026-08-30T04:34:59.432186+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "416da08f569d583eb54f4445950e31c937e8c49478d964ca9ecc78ad8138b777", "fetched_at": "2026-08-28T04:04:48.785455+00:00", "kind": "readme", "missing": false, "url": "https://github.com/epfml/OptML_course"}], "taxonomy_version": 1}, "when_to_choose": {"confidence": null, "enriched_at": "2026-08-30T04:34:59.432186+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "416da08f569d583eb54f4445950e31c937e8c49478d964ca9ecc78ad8138b777", "fetched_at": "2026-08-28T04:04:48.785455+00:00", "kind": "readme", "missing": false, "url": "https://github.com/epfml/OptML_course"}], "taxonomy_version": 1}}, "score": {"components": {"activity": 92, "longevity": 100, "rhythm": 35}, "computed_at": "2026-09-03T02:20:16.233290+00:00", "flags": ["no_releases", "no_license"], "formula": "round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)", "inputs": {"age_days": 3115, "days_push": 50, "days_rel": null, "gap_med": null, "n_releases_24m": 0}, "score": 74, "version": 2}, "staleness": {"enrichment_outdated": false, "low_confidence": false, "scrape_days": 9, "stale_scrape": false}}