{"adoption": {"forks": 1372, "observed_at": "2026-08-28T04:10:26.235866+00:00", "stars": 8907}, "canonical_url": "https://ross.abutalabs.com/products/deep-learning-interview-book", "card": {"archived": false, "artifact_type": "learning-resource", "description": "深度学习面试宝典（含数学、机器学习、深度学习、计算机视觉、自然语言处理和SLAM等方向）", "domain": ["deep-learning", "machine-learning", "computer-vision", "tutorials"], "enriched": true, "function": ["documentation", "developer-tools"], "health_score": 20, "homepage": null, "language": null, "license": null, "license_family": "other", "maturity": "active", "member_repos": ["amusi/Deep-Learning-Interview-Book"], "name": "amusi/Deep-Learning-Interview-Book", "platform": ["cross-platform"], "pushed_at": "2024-04-24T12:09:45+00:00", "repo": "amusi/Deep-Learning-Interview-Book", "stars": 8907, "tags": ["interview-preparation", "ai-jobs", "study-notes", "slam", "recommendation-systems", "chinese-language", "natural-language-processing"], "topics": ["deep-learning", "interview", "machine-learning", "computer-vision", "natural-language-processing", "slam", "recommendation-system"], "urls": [], "use_cases": ["prepare for a deep learning engineer interview", "study machine learning interview questions", "review computer vision interview topics", "practice NLP interview questions", "brush up on data structures and algorithms for AI job interviews", "find AI algorithm engineer job hunting tips"], "what_it_is": "A curated Chinese-language interview preparation book for AI roles, covering math, machine learning, deep learning, computer vision, NLP, SLAM, recommendation systems, and coding questions. It is organized as a collection of markdown study notes plus links to job-hunting resources.", "when_to_avoid": ["you need a formal textbook or structured course with exercises", "you only read English, since the content is primarily in Chinese", "you want hands-on code projects rather than conceptual Q&A notes"], "when_to_choose": ["you are preparing for AI/ML algorithm engineer interviews, especially in the Chinese job market", "you want a broad, topic-organized collection of interview questions and answers", "you need coverage of niche areas like SLAM or recommendation algorithms"]}, "data_as_of": "2026-08-30T08:39:29.467469+00:00", "members": [{"path": "/products/deep-learning-interview-book", "repo": "amusi/Deep-Learning-Interview-Book", "role": "main", "score": 32}], "provenance": {"archived": {"kind": "observed", "observed_at": "2026-08-28T04:10:26.235866+00:00", "source": "github"}, "artifact_type": {"confidence": null, "enriched_at": "2026-08-29T17:24:43.357511+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "37f2f7b876abbc54481bb1709ce8f1aa95870a91c1a11b500e7efc81e295065f", "fetched_at": "2026-08-28T04:10:26.235866+00:00", "kind": "readme", "missing": false, "url": "https://github.com/amusi/Deep-Learning-Interview-Book"}], "taxonomy_version": 1}, "description": {"kind": "observed", "observed_at": "2026-08-28T04:10:26.235866+00:00", "source": "github"}, "domain": {"confidence": null, "enriched_at": "2026-08-29T17:24:43.357511+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "37f2f7b876abbc54481bb1709ce8f1aa95870a91c1a11b500e7efc81e295065f", "fetched_at": "2026-08-28T04:10:26.235866+00:00", "kind": "readme", "missing": false, "url": "https://github.com/amusi/Deep-Learning-Interview-Book"}], "taxonomy_version": 1}, "enriched": {"inputs": [], "kind": "computed", "method": "enrichment_status"}, "function": {"confidence": null, "enriched_at": "2026-08-29T17:24:43.357511+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "37f2f7b876abbc54481bb1709ce8f1aa95870a91c1a11b500e7efc81e295065f", "fetched_at": "2026-08-28T04:10:26.235866+00:00", "kind": "readme", "missing": false, "url": "https://github.com/amusi/Deep-Learning-Interview-Book"}], "taxonomy_version": 1}, "health_score": {"inputs": ["days_since_push", "days_since_release", "archived"], "kind": "computed", "method": "health_v1"}, "homepage": {"kind": "observed", "observed_at": "2026-08-28T04:10:26.235866+00:00", "source": "github"}, "language": {"kind": "observed", "observed_at": "2026-08-28T04:10:26.235866+00:00", "source": "github"}, "license": {"kind": "observed", "observed_at": "2026-08-28T04:10:26.235866+00:00", "source": "github"}, "license_family": {"inputs": ["license"], "kind": "computed", "method": "license_family"}, "maturity": {"confidence": null, "enriched_at": "2026-08-29T17:24:43.357511+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "37f2f7b876abbc54481bb1709ce8f1aa95870a91c1a11b500e7efc81e295065f", "fetched_at": "2026-08-28T04:10:26.235866+00:00", "kind": "readme", "missing": false, "url": "https://github.com/amusi/Deep-Learning-Interview-Book"}], "taxonomy_version": 1}, "member_repos": {"kind": "observed", "observed_at": "2026-08-28T04:10:26.235866+00:00", "source": "github"}, "name": {"kind": "observed", "observed_at": "2026-08-28T04:10:26.235866+00:00", "source": "github"}, "platform": {"confidence": null, "enriched_at": "2026-08-29T17:24:43.357511+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "37f2f7b876abbc54481bb1709ce8f1aa95870a91c1a11b500e7efc81e295065f", "fetched_at": "2026-08-28T04:10:26.235866+00:00", "kind": "readme", "missing": false, "url": "https://github.com/amusi/Deep-Learning-Interview-Book"}], "taxonomy_version": 1}, "pushed_at": {"kind": "observed", "observed_at": "2026-08-28T04:10:26.235866+00:00", "source": "github"}, "repo": {"kind": "observed", "observed_at": "2026-08-28T04:10:26.235866+00:00", "source": "github"}, "stars": {"kind": "observed", "observed_at": "2026-08-28T04:10:26.235866+00:00", "source": "github"}, "tags": {"confidence": null, "enriched_at": "2026-08-29T17:24:43.357511+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "37f2f7b876abbc54481bb1709ce8f1aa95870a91c1a11b500e7efc81e295065f", "fetched_at": "2026-08-28T04:10:26.235866+00:00", "kind": "readme", "missing": false, "url": "https://github.com/amusi/Deep-Learning-Interview-Book"}], "taxonomy_version": 1}, "topics": {"kind": "observed", "observed_at": "2026-08-28T04:10:26.235866+00:00", "source": "github"}, "urls": {"kind": "observed", "observed_at": "2026-08-28T04:10:26.235866+00:00", "source": "github"}, "use_cases": {"confidence": null, "enriched_at": "2026-08-29T17:24:43.357511+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "37f2f7b876abbc54481bb1709ce8f1aa95870a91c1a11b500e7efc81e295065f", "fetched_at": "2026-08-28T04:10:26.235866+00:00", "kind": "readme", "missing": false, "url": "https://github.com/amusi/Deep-Learning-Interview-Book"}], "taxonomy_version": 1}, "what_it_is": {"confidence": null, "enriched_at": "2026-08-29T17:24:43.357511+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "37f2f7b876abbc54481bb1709ce8f1aa95870a91c1a11b500e7efc81e295065f", "fetched_at": "2026-08-28T04:10:26.235866+00:00", "kind": "readme", "missing": false, "url": "https://github.com/amusi/Deep-Learning-Interview-Book"}], "taxonomy_version": 1}, "when_to_avoid": {"confidence": null, "enriched_at": "2026-08-29T17:24:43.357511+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "37f2f7b876abbc54481bb1709ce8f1aa95870a91c1a11b500e7efc81e295065f", "fetched_at": "2026-08-28T04:10:26.235866+00:00", "kind": "readme", "missing": false, "url": "https://github.com/amusi/Deep-Learning-Interview-Book"}], "taxonomy_version": 1}, "when_to_choose": {"confidence": null, "enriched_at": "2026-08-29T17:24:43.357511+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "37f2f7b876abbc54481bb1709ce8f1aa95870a91c1a11b500e7efc81e295065f", "fetched_at": "2026-08-28T04:10:26.235866+00:00", "kind": "readme", "missing": false, "url": "https://github.com/amusi/Deep-Learning-Interview-Book"}], "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": 2627, "days_push": 861, "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}}