{"adoption": {"forks": 678, "observed_at": "2026-08-28T04:06:25.179065+00:00", "stars": 2196}, "canonical_url": "https://ross.abutalabs.com/products/deep-learning-with-keras-notebooks", "card": {"archived": false, "artifact_type": "learning-resource", "description": "Jupyter notebooks for using & learning Keras", "domain": ["deep-learning", "machine-learning", "computer-vision", "tutorials"], "enriched": true, "function": ["deep-learning", "machine-learning", "image-processing", "data-visualization"], "health_score": 20, "homepage": null, "language": "Jupyter Notebook", "license": null, "license_family": "other", "maturity": "maintenance", "member_repos": ["erhwenkuo/deep-learning-with-keras-notebooks"], "name": "erhwenkuo/deep-learning-with-keras-notebooks", "platform": ["python", "windows", "cross-platform"], "pushed_at": "2018-12-19T17:12:14+00:00", "repo": "erhwenkuo/deep-learning-with-keras-notebooks", "stars": 2196, "tags": ["keras", "jupyter-notebooks", "tensorflow", "cnn", "rnn", "lstm", "autoencoder", "image-classification", "transfer-learning", "chinese-language", "natural-language-processing", "gpu"], "topics": ["deep-learning", "keras-notebooks", "keras"], "urls": [], "use_cases": ["learn keras with example notebooks", "deep learning tutorials for beginners", "image classification with pretrained models", "understand lstm and rnn basics", "build autoencoders in keras", "image augmentation for small datasets", "visualize what convnets learn"], "what_it_is": "A collection of Jupyter notebooks documenting one developer's learning of Keras, covering CNNs, RNNs/LSTMs, autoencoders, seq2seq, image augmentation, and pretrained models. It serves as a hands-on tutorial resource for people getting started with deep learning using Keras and TensorFlow.", "when_to_avoid": ["you need a maintained library or up-to-date Keras/TensorFlow APIs (last updated 2018)", "you need production code or a license permitting reuse", "you want modern frameworks like PyTorch or current Keras 3"], "when_to_choose": ["you want hands-on, notebook-style examples for learning Keras", "you need beginner-friendly deep learning walkthroughs including Chinese-language explanations", "you want examples of CNNs, RNNs, autoencoders, and transfer learning"]}, "data_as_of": "2026-08-30T08:39:29.467469+00:00", "members": [{"path": "/products/deep-learning-with-keras-notebooks", "repo": "erhwenkuo/deep-learning-with-keras-notebooks", "role": "main", "score": 32}], "provenance": {"archived": {"kind": "observed", "observed_at": "2026-08-28T04:06:25.179065+00:00", "source": "github"}, "artifact_type": {"confidence": null, "enriched_at": "2026-08-30T02:47:13.326418+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "6dc4b64495a23c6a13f37722a85ceec1d0abee4dbad85c8f03ad9dfed6dc5ca5", "fetched_at": "2026-08-28T04:06:25.179065+00:00", "kind": "readme", "missing": false, "url": "https://github.com/erhwenkuo/deep-learning-with-keras-notebooks"}], "taxonomy_version": 1}, "description": {"kind": "observed", "observed_at": "2026-08-28T04:06:25.179065+00:00", "source": "github"}, "domain": {"confidence": null, "enriched_at": "2026-08-30T02:47:13.326418+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "6dc4b64495a23c6a13f37722a85ceec1d0abee4dbad85c8f03ad9dfed6dc5ca5", "fetched_at": "2026-08-28T04:06:25.179065+00:00", "kind": "readme", "missing": false, "url": "https://github.com/erhwenkuo/deep-learning-with-keras-notebooks"}], "taxonomy_version": 1}, "enriched": {"inputs": [], "kind": "computed", "method": "enrichment_status"}, "function": {"confidence": null, "enriched_at": "2026-08-30T02:47:13.326418+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "6dc4b64495a23c6a13f37722a85ceec1d0abee4dbad85c8f03ad9dfed6dc5ca5", "fetched_at": "2026-08-28T04:06:25.179065+00:00", "kind": "readme", "missing": false, "url": "https://github.com/erhwenkuo/deep-learning-with-keras-notebooks"}], "taxonomy_version": 1}, "health_score": {"inputs": ["days_since_push", "days_since_release", "archived"], "kind": "computed", "method": "health_v1"}, "homepage": {"kind": "observed", "observed_at": "2026-08-28T04:06:25.179065+00:00", "source": "github"}, "language": {"kind": "observed", "observed_at": "2026-08-28T04:06:25.179065+00:00", "source": "github"}, "license": {"kind": "observed", "observed_at": "2026-08-28T04:06:25.179065+00:00", "source": "github"}, "license_family": {"inputs": ["license"], "kind": "computed", "method": "license_family"}, "maturity": {"confidence": null, "enriched_at": "2026-08-30T02:47:13.326418+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "6dc4b64495a23c6a13f37722a85ceec1d0abee4dbad85c8f03ad9dfed6dc5ca5", "fetched_at": "2026-08-28T04:06:25.179065+00:00", "kind": "readme", "missing": false, "url": "https://github.com/erhwenkuo/deep-learning-with-keras-notebooks"}], "taxonomy_version": 1}, "member_repos": {"kind": "observed", "observed_at": "2026-08-28T04:06:25.179065+00:00", "source": "github"}, "name": {"kind": "observed", "observed_at": "2026-08-28T04:06:25.179065+00:00", "source": "github"}, "platform": {"confidence": null, "enriched_at": "2026-08-30T02:47:13.326418+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "6dc4b64495a23c6a13f37722a85ceec1d0abee4dbad85c8f03ad9dfed6dc5ca5", "fetched_at": "2026-08-28T04:06:25.179065+00:00", "kind": "readme", "missing": false, "url": "https://github.com/erhwenkuo/deep-learning-with-keras-notebooks"}], "taxonomy_version": 1}, "pushed_at": {"kind": "observed", "observed_at": "2026-08-28T04:06:25.179065+00:00", "source": "github"}, "repo": {"kind": "observed", "observed_at": "2026-08-28T04:06:25.179065+00:00", "source": "github"}, "stars": {"kind": "observed", "observed_at": "2026-08-28T04:06:25.179065+00:00", "source": "github"}, "tags": {"confidence": null, "enriched_at": "2026-08-30T02:47:13.326418+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "6dc4b64495a23c6a13f37722a85ceec1d0abee4dbad85c8f03ad9dfed6dc5ca5", "fetched_at": "2026-08-28T04:06:25.179065+00:00", "kind": "readme", "missing": false, "url": "https://github.com/erhwenkuo/deep-learning-with-keras-notebooks"}], "taxonomy_version": 1}, "topics": {"kind": "observed", "observed_at": "2026-08-28T04:06:25.179065+00:00", "source": "github"}, "urls": {"kind": "observed", "observed_at": "2026-08-28T04:06:25.179065+00:00", "source": "github"}, "use_cases": {"confidence": null, "enriched_at": "2026-08-30T02:47:13.326418+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "6dc4b64495a23c6a13f37722a85ceec1d0abee4dbad85c8f03ad9dfed6dc5ca5", "fetched_at": "2026-08-28T04:06:25.179065+00:00", "kind": "readme", "missing": false, "url": "https://github.com/erhwenkuo/deep-learning-with-keras-notebooks"}], "taxonomy_version": 1}, "what_it_is": {"confidence": null, "enriched_at": "2026-08-30T02:47:13.326418+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "6dc4b64495a23c6a13f37722a85ceec1d0abee4dbad85c8f03ad9dfed6dc5ca5", "fetched_at": "2026-08-28T04:06:25.179065+00:00", "kind": "readme", "missing": false, "url": "https://github.com/erhwenkuo/deep-learning-with-keras-notebooks"}], "taxonomy_version": 1}, "when_to_avoid": {"confidence": null, "enriched_at": "2026-08-30T02:47:13.326418+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "6dc4b64495a23c6a13f37722a85ceec1d0abee4dbad85c8f03ad9dfed6dc5ca5", "fetched_at": "2026-08-28T04:06:25.179065+00:00", "kind": "readme", "missing": false, "url": "https://github.com/erhwenkuo/deep-learning-with-keras-notebooks"}], "taxonomy_version": 1}, "when_to_choose": {"confidence": null, "enriched_at": "2026-08-30T02:47:13.326418+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "6dc4b64495a23c6a13f37722a85ceec1d0abee4dbad85c8f03ad9dfed6dc5ca5", "fetched_at": "2026-08-28T04:06:25.179065+00:00", "kind": "readme", "missing": false, "url": "https://github.com/erhwenkuo/deep-learning-with-keras-notebooks"}], "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": 3221, "days_push": 2814, "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}}