{"adoption": {"forks": 508, "observed_at": "2026-08-28T04:04:51.348881+00:00", "stars": 1484}, "canonical_url": "https://ross.abutalabs.com/products/pytorchstepbystep", "card": {"archived": false, "artifact_type": "learning-resource", "description": "Official repository of my book: \"Deep Learning with PyTorch Step-by-Step: A Beginner's Guide\"", "domain": ["deep-learning", "machine-learning", "tutorials", "computer-vision"], "enriched": true, "function": ["deep-learning", "machine-learning", "nlp", "computer-vision"], "health_score": 61, "homepage": "https://pytorchstepbystep.com", "language": "Jupyter Notebook", "license": "MIT", "license_family": "permissive", "maturity": "active", "member_repos": ["dvgodoy/PyTorchStepByStep"], "name": "dvgodoy/PyTorchStepByStep", "platform": ["python", "browser"], "pushed_at": "2026-02-19T21:12:52+00:00", "repo": "dvgodoy/PyTorchStepByStep", "stars": 1484, "tags": ["pytorch", "jupyter-notebooks", "book-companion", "beginner-friendly", "gradient-descent", "cnn", "rnn", "transformers", "huggingface", "google-colab", "natural-language-processing", "gpu"], "topics": ["deep-learning", "pytorch", "pytorch-tutorial", "python", "cnn-pytorch", "rnn-pytorch"], "urls": [], "use_cases": ["learn pytorch from scratch", "understand how gradient descent and training loops work", "learn convolutional neural networks in pytorch", "learn rnn lstm and seq2seq models", "fine-tune bert and gpt-2 with huggingface", "run pytorch tutorials in google colab with free gpu", "beginner-friendly deep learning book with runnable code"], "what_it_is": "The official companion repository for the book 'Deep Learning with PyTorch Step-by-Step: A Beginner's Guide', containing one runnable Jupyter notebook per chapter. It teaches PyTorch from first principles, covering gradient descent through CNNs, RNNs, and fine-tuning NLP models with HuggingFace.", "when_to_avoid": ["you need a production PyTorch library or framework rather than learning material", "you want a quick image-classification-only tutorial without fundamentals", "you need a formal, mathematically rigorous textbook treatment"], "when_to_choose": ["you want a structured, incremental, from-first-principles introduction to PyTorch", "you prefer conversational, plain-English explanations over heavy math notation", "you want reproducible notebooks matching book outputs", "you are a beginner starting deep learning with PyTorch 2.x"]}, "data_as_of": "2026-08-30T08:39:29.467469+00:00", "members": [{"path": "/products/pytorchstepbystep", "repo": "dvgodoy/PyTorchStepByStep", "role": "main", "score": 63}], "provenance": {"archived": {"kind": "observed", "observed_at": "2026-08-28T04:04:51.348881+00:00", "source": "github"}, "artifact_type": {"confidence": null, "enriched_at": "2026-08-30T04:34:00.234804+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "1cd98e78278b3faf800cf9055fb5140ce4ba66a674d763e5a894113670b6f841", "fetched_at": "2026-08-28T04:04:51.348881+00:00", "kind": "readme", "missing": false, "url": "https://github.com/dvgodoy/PyTorchStepByStep"}, {"content_hash": "9e7c24fe307e2ed7864691c188e980c786adc09dcba1c26c1433978ad862be5b", "fetched_at": "2026-08-29T11:40:20.332473+00:00", "kind": "homepage", "missing": false, "url": "https://pytorchstepbystep.com"}], "taxonomy_version": 1}, "description": {"kind": "observed", "observed_at": "2026-08-28T04:04:51.348881+00:00", "source": "github"}, "domain": {"confidence": null, "enriched_at": "2026-08-30T04:34:00.234804+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "1cd98e78278b3faf800cf9055fb5140ce4ba66a674d763e5a894113670b6f841", "fetched_at": "2026-08-28T04:04:51.348881+00:00", "kind": "readme", "missing": false, "url": "https://github.com/dvgodoy/PyTorchStepByStep"}, {"content_hash": "9e7c24fe307e2ed7864691c188e980c786adc09dcba1c26c1433978ad862be5b", "fetched_at": "2026-08-29T11:40:20.332473+00:00", "kind": "homepage", "missing": false, "url": "https://pytorchstepbystep.com"}], "taxonomy_version": 1}, "enriched": {"inputs": [], "kind": "computed", "method": "enrichment_status"}, "function": {"confidence": null, "enriched_at": "2026-08-30T04:34:00.234804+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "1cd98e78278b3faf800cf9055fb5140ce4ba66a674d763e5a894113670b6f841", "fetched_at": "2026-08-28T04:04:51.348881+00:00", "kind": "readme", "missing": false, "url": "https://github.com/dvgodoy/PyTorchStepByStep"}, {"content_hash": "9e7c24fe307e2ed7864691c188e980c786adc09dcba1c26c1433978ad862be5b", "fetched_at": "2026-08-29T11:40:20.332473+00:00", "kind": "homepage", "missing": false, "url": "https://pytorchstepbystep.com"}], "taxonomy_version": 1}, "health_score": {"inputs": ["days_since_push", "days_since_release", "archived"], "kind": "computed", "method": "health_v1"}, "homepage": {"kind": "observed", "observed_at": "2026-08-28T04:04:51.348881+00:00", "source": "github"}, "language": {"kind": "observed", "observed_at": "2026-08-28T04:04:51.348881+00:00", "source": "github"}, "license": {"kind": "observed", "observed_at": "2026-08-28T04:04:51.348881+00:00", "source": "github"}, "license_family": {"inputs": ["license"], "kind": "computed", "method": "license_family"}, "maturity": {"confidence": null, "enriched_at": "2026-08-30T04:34:00.234804+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "1cd98e78278b3faf800cf9055fb5140ce4ba66a674d763e5a894113670b6f841", "fetched_at": "2026-08-28T04:04:51.348881+00:00", "kind": "readme", "missing": false, "url": "https://github.com/dvgodoy/PyTorchStepByStep"}, {"content_hash": "9e7c24fe307e2ed7864691c188e980c786adc09dcba1c26c1433978ad862be5b", "fetched_at": "2026-08-29T11:40:20.332473+00:00", "kind": "homepage", "missing": false, "url": "https://pytorchstepbystep.com"}], "taxonomy_version": 1}, "member_repos": {"kind": "observed", "observed_at": "2026-08-28T04:04:51.348881+00:00", "source": "github"}, "name": {"kind": "observed", "observed_at": "2026-08-28T04:04:51.348881+00:00", "source": "github"}, "platform": {"confidence": null, "enriched_at": "2026-08-30T04:34:00.234804+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "1cd98e78278b3faf800cf9055fb5140ce4ba66a674d763e5a894113670b6f841", "fetched_at": "2026-08-28T04:04:51.348881+00:00", "kind": "readme", "missing": false, "url": "https://github.com/dvgodoy/PyTorchStepByStep"}, {"content_hash": "9e7c24fe307e2ed7864691c188e980c786adc09dcba1c26c1433978ad862be5b", "fetched_at": "2026-08-29T11:40:20.332473+00:00", "kind": "homepage", "missing": false, "url": "https://pytorchstepbystep.com"}], "taxonomy_version": 1}, "pushed_at": {"kind": "observed", "observed_at": "2026-08-28T04:04:51.348881+00:00", "source": "github"}, "repo": {"kind": "observed", "observed_at": "2026-08-28T04:04:51.348881+00:00", "source": "github"}, "stars": {"kind": "observed", "observed_at": "2026-08-28T04:04:51.348881+00:00", "source": "github"}, "tags": {"confidence": null, "enriched_at": "2026-08-30T04:34:00.234804+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "1cd98e78278b3faf800cf9055fb5140ce4ba66a674d763e5a894113670b6f841", "fetched_at": "2026-08-28T04:04:51.348881+00:00", "kind": "readme", "missing": false, "url": "https://github.com/dvgodoy/PyTorchStepByStep"}, {"content_hash": "9e7c24fe307e2ed7864691c188e980c786adc09dcba1c26c1433978ad862be5b", "fetched_at": "2026-08-29T11:40:20.332473+00:00", "kind": "homepage", "missing": false, "url": "https://pytorchstepbystep.com"}], "taxonomy_version": 1}, "topics": {"kind": "observed", "observed_at": "2026-08-28T04:04:51.348881+00:00", "source": "github"}, "urls": {"kind": "observed", "observed_at": "2026-08-28T04:04:51.348881+00:00", "source": "github"}, "use_cases": {"confidence": null, "enriched_at": "2026-08-30T04:34:00.234804+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "1cd98e78278b3faf800cf9055fb5140ce4ba66a674d763e5a894113670b6f841", "fetched_at": "2026-08-28T04:04:51.348881+00:00", "kind": "readme", "missing": false, "url": "https://github.com/dvgodoy/PyTorchStepByStep"}, {"content_hash": "9e7c24fe307e2ed7864691c188e980c786adc09dcba1c26c1433978ad862be5b", "fetched_at": "2026-08-29T11:40:20.332473+00:00", "kind": "homepage", "missing": false, "url": "https://pytorchstepbystep.com"}], "taxonomy_version": 1}, "what_it_is": {"confidence": null, "enriched_at": "2026-08-30T04:34:00.234804+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "1cd98e78278b3faf800cf9055fb5140ce4ba66a674d763e5a894113670b6f841", "fetched_at": "2026-08-28T04:04:51.348881+00:00", "kind": "readme", "missing": false, "url": "https://github.com/dvgodoy/PyTorchStepByStep"}, {"content_hash": "9e7c24fe307e2ed7864691c188e980c786adc09dcba1c26c1433978ad862be5b", "fetched_at": "2026-08-29T11:40:20.332473+00:00", "kind": "homepage", "missing": false, "url": "https://pytorchstepbystep.com"}], "taxonomy_version": 1}, "when_to_avoid": {"confidence": null, "enriched_at": "2026-08-30T04:34:00.234804+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "1cd98e78278b3faf800cf9055fb5140ce4ba66a674d763e5a894113670b6f841", "fetched_at": "2026-08-28T04:04:51.348881+00:00", "kind": "readme", "missing": false, "url": "https://github.com/dvgodoy/PyTorchStepByStep"}, {"content_hash": "9e7c24fe307e2ed7864691c188e980c786adc09dcba1c26c1433978ad862be5b", "fetched_at": "2026-08-29T11:40:20.332473+00:00", "kind": "homepage", "missing": false, "url": "https://pytorchstepbystep.com"}], "taxonomy_version": 1}, "when_to_choose": {"confidence": null, "enriched_at": "2026-08-30T04:34:00.234804+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "1cd98e78278b3faf800cf9055fb5140ce4ba66a674d763e5a894113670b6f841", "fetched_at": "2026-08-28T04:04:51.348881+00:00", "kind": "readme", "missing": false, "url": "https://github.com/dvgodoy/PyTorchStepByStep"}, {"content_hash": "9e7c24fe307e2ed7864691c188e980c786adc09dcba1c26c1433978ad862be5b", "fetched_at": "2026-08-29T11:40:20.332473+00:00", "kind": "homepage", "missing": false, "url": "https://pytorchstepbystep.com"}], "taxonomy_version": 1}}, "score": {"components": {"activity": 68, "longevity": 100, "rhythm": 35}, "computed_at": "2026-09-03T02:20:16.233290+00:00", "flags": ["no_releases"], "formula": "round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)", "inputs": {"age_days": 2332, "days_push": 195, "days_rel": null, "gap_med": null, "n_releases_24m": 0}, "score": 63, "version": 2}, "staleness": {"enrichment_outdated": false, "low_confidence": false, "scrape_days": 9, "stale_scrape": false}}