# adam-maj/deep-learning

A deep-dive on the entire history of deep-learning

Repository: https://github.com/adam-maj/deep-learning
Canonical: https://ross.abutalabs.com/products/adam-maj-deep-learning
Language: Jupyter Notebook
License Family: other
Last push: 2024-07-16T04:58:29+00:00

## Health v2 (maintenance only)
Score: 24/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 59
- inputs: {"age_days": 838, "days_push": 778, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1575, forks 124 (observed 2026-08-28T04:05:05.849103+00:00)

## What it is
An educational repository tracing the full history of deep learning, from feed-forward networks to GPT-4o, organized around seven key constraints on AI progress. It pairs curated papers and notes with toy PyTorch implementations in Jupyter notebooks.

## Use cases
- learn the history of deep learning innovations
- understand how transformers and GPT models evolved
- find key papers behind major deep learning milestones
- study toy PyTorch implementations of neural network architectures
- get an intuitive explanation of deep learning math and concepts
- prepare for deep learning interviews or coursework

## When to choose
- you want a curated, narrative-driven tour of deep learning history
- you prefer concise explanations plus minimal code over full frameworks
- you want links to the original papers for each milestone

## When to avoid
- you need production-ready model code or a maintained library
- you want comprehensive step-by-step tutorials with exercises
- you need a license-clear resource for redistribution, since no license is provided

## Facets
- artifact type: learning-resource
- maturity: stable
- function: deep-learning, machine-learning, llm-training
- domain: deep-learning, large-language-models, tutorials, artificial-intelligence
- platform: python
- tags: educational, jupyter-notebooks, pytorch, history-of-ai, papers, neural-networks

## Member repositories
- adam-maj/deep-learning (main) score 24

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:05.849103+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:57:35.209772+00:00, confidence not recorded.
  - readme: https://github.com/adam-maj/deep-learning (fetched 2026-08-28T04:05:05.849103+00:00, sha 9bd419eec46a)
- Data as of 2026-08-30T08:39:29.467469+00:00.