{"adoption": {"forks": 217, "observed_at": "2026-08-28T04:05:23.631073+00:00", "stars": 1695}, "canonical_url": "https://ross.abutalabs.com/products/zhengjingwei-machine-learning-interview", "card": {"archived": false, "artifact_type": "learning-resource", "description": "算法工程师-机器学习面试题总结", "domain": ["machine-learning", "tutorials", "education"], "enriched": true, "function": ["machine-learning", "deep-learning", "nlp"], "health_score": 20, "homepage": null, "language": null, "license": null, "license_family": "other", "maturity": "maintenance", "member_repos": ["zhengjingwei/machine-learning-interview"], "name": "zhengjingwei/machine-learning-interview", "platform": ["cross-platform"], "pushed_at": "2019-09-26T13:16:03+00:00", "repo": "zhengjingwei/machine-learning-interview", "stars": 1695, "tags": ["interview-preparation", "question-bank", "chinese-language", "study-notes"], "topics": ["machine-learning", "deep-learning", "interview"], "urls": [], "use_cases": ["prepare for a machine learning engineer interview", "review classic ML algorithm concepts before an interview", "study feature engineering interview questions", "practice deep learning interview questions", "refresh knowledge of SVM, KNN, and Naive Bayes theory"], "what_it_is": "A curated collection of machine learning and deep learning interview questions with answers, written primarily in Chinese. It covers fundamentals like loss functions, evaluation metrics, feature engineering, and classic algorithms such as KNN, SVM, and Naive Bayes.", "when_to_avoid": ["you need a runnable library or code tool", "you need up-to-date content on LLMs or modern deep learning topics", "you need English-language material or a maintained, actively updated resource"], "when_to_choose": ["you are preparing for algorithm/ML engineer interviews and want a structured question bank", "you prefer Chinese-language study material", "you want a checklist-style review of ML fundamentals"]}, "data_as_of": "2026-08-30T08:39:29.467469+00:00", "members": [{"path": "/products/zhengjingwei-machine-learning-interview", "repo": "zhengjingwei/machine-learning-interview", "role": "main", "score": 32}], "provenance": {"archived": {"kind": "observed", "observed_at": "2026-08-28T04:05:23.631073+00:00", "source": "github"}, "artifact_type": {"confidence": null, "enriched_at": "2026-08-30T03:37:58.559081+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "db16ebb49b79ce04873b6759ef4c84b20af265de7b6f86ffbd60526c716171c7", "fetched_at": "2026-08-28T04:05:23.631073+00:00", "kind": "readme", "missing": false, "url": "https://github.com/zhengjingwei/machine-learning-interview"}], "taxonomy_version": 1}, "description": {"kind": "observed", "observed_at": "2026-08-28T04:05:23.631073+00:00", "source": "github"}, "domain": {"confidence": null, "enriched_at": "2026-08-30T03:37:58.559081+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "db16ebb49b79ce04873b6759ef4c84b20af265de7b6f86ffbd60526c716171c7", "fetched_at": "2026-08-28T04:05:23.631073+00:00", "kind": "readme", "missing": false, "url": "https://github.com/zhengjingwei/machine-learning-interview"}], "taxonomy_version": 1}, "enriched": {"inputs": [], "kind": "computed", "method": "enrichment_status"}, "function": {"confidence": null, "enriched_at": "2026-08-30T03:37:58.559081+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "db16ebb49b79ce04873b6759ef4c84b20af265de7b6f86ffbd60526c716171c7", "fetched_at": "2026-08-28T04:05:23.631073+00:00", "kind": "readme", "missing": false, "url": "https://github.com/zhengjingwei/machine-learning-interview"}], "taxonomy_version": 1}, "health_score": {"inputs": ["days_since_push", "days_since_release", "archived"], "kind": "computed", "method": "health_v1"}, "homepage": {"kind": "observed", "observed_at": "2026-08-28T04:05:23.631073+00:00", "source": "github"}, "language": {"kind": "observed", "observed_at": "2026-08-28T04:05:23.631073+00:00", "source": "github"}, "license": {"kind": "observed", "observed_at": "2026-08-28T04:05:23.631073+00:00", "source": "github"}, "license_family": {"inputs": ["license"], "kind": "computed", "method": "license_family"}, "maturity": {"confidence": null, "enriched_at": "2026-08-30T03:37:58.559081+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "db16ebb49b79ce04873b6759ef4c84b20af265de7b6f86ffbd60526c716171c7", "fetched_at": "2026-08-28T04:05:23.631073+00:00", "kind": "readme", "missing": false, "url": "https://github.com/zhengjingwei/machine-learning-interview"}], "taxonomy_version": 1}, "member_repos": {"kind": "observed", "observed_at": "2026-08-28T04:05:23.631073+00:00", "source": "github"}, "name": {"kind": "observed", "observed_at": "2026-08-28T04:05:23.631073+00:00", "source": "github"}, "platform": {"confidence": null, "enriched_at": "2026-08-30T03:37:58.559081+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "db16ebb49b79ce04873b6759ef4c84b20af265de7b6f86ffbd60526c716171c7", "fetched_at": "2026-08-28T04:05:23.631073+00:00", "kind": "readme", "missing": false, "url": "https://github.com/zhengjingwei/machine-learning-interview"}], "taxonomy_version": 1}, "pushed_at": {"kind": "observed", "observed_at": "2026-08-28T04:05:23.631073+00:00", "source": "github"}, "repo": {"kind": "observed", "observed_at": "2026-08-28T04:05:23.631073+00:00", "source": "github"}, "stars": {"kind": "observed", "observed_at": "2026-08-28T04:05:23.631073+00:00", "source": "github"}, "tags": {"confidence": null, "enriched_at": "2026-08-30T03:37:58.559081+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "db16ebb49b79ce04873b6759ef4c84b20af265de7b6f86ffbd60526c716171c7", "fetched_at": "2026-08-28T04:05:23.631073+00:00", "kind": "readme", "missing": false, "url": "https://github.com/zhengjingwei/machine-learning-interview"}], "taxonomy_version": 1}, "topics": {"kind": "observed", "observed_at": "2026-08-28T04:05:23.631073+00:00", "source": "github"}, "urls": {"kind": "observed", "observed_at": "2026-08-28T04:05:23.631073+00:00", "source": "github"}, "use_cases": {"confidence": null, "enriched_at": "2026-08-30T03:37:58.559081+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "db16ebb49b79ce04873b6759ef4c84b20af265de7b6f86ffbd60526c716171c7", "fetched_at": "2026-08-28T04:05:23.631073+00:00", "kind": "readme", "missing": false, "url": "https://github.com/zhengjingwei/machine-learning-interview"}], "taxonomy_version": 1}, "what_it_is": {"confidence": null, "enriched_at": "2026-08-30T03:37:58.559081+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "db16ebb49b79ce04873b6759ef4c84b20af265de7b6f86ffbd60526c716171c7", "fetched_at": "2026-08-28T04:05:23.631073+00:00", "kind": "readme", "missing": false, "url": "https://github.com/zhengjingwei/machine-learning-interview"}], "taxonomy_version": 1}, "when_to_avoid": {"confidence": null, "enriched_at": "2026-08-30T03:37:58.559081+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "db16ebb49b79ce04873b6759ef4c84b20af265de7b6f86ffbd60526c716171c7", "fetched_at": "2026-08-28T04:05:23.631073+00:00", "kind": "readme", "missing": false, "url": "https://github.com/zhengjingwei/machine-learning-interview"}], "taxonomy_version": 1}, "when_to_choose": {"confidence": null, "enriched_at": "2026-08-30T03:37:58.559081+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "db16ebb49b79ce04873b6759ef4c84b20af265de7b6f86ffbd60526c716171c7", "fetched_at": "2026-08-28T04:05:23.631073+00:00", "kind": "readme", "missing": false, "url": "https://github.com/zhengjingwei/machine-learning-interview"}], "taxonomy_version": 1}}, "score": {"components": {"activity": 0, "longevity": 100, "rhythm": 35}, "computed_at": "2026-09-02T17:46:02.011165+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": 2704, "days_push": 2533, "days_rel": null, "gap_med": null, "n_releases_24m": 0}, "score": 32, "version": 2}, "staleness": {"enrichment_outdated": false, "low_confidence": false, "scrape_days": 9, "stale_scrape": false}}