# Dive into Deep Learning (D2L.ai)

Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.

Repository: https://github.com/d2l-ai/d2l-en
Canonical: https://ross.abutalabs.com/products/dive-into-deep-learning-d2lai
Homepage: https://D2L.ai
Language: Python
License: NOASSERTION
License Family: other
Topics: deep-learning, machine-learning, book, notebook, computer-vision, natural-language-processing, python, kaggle, data-science, mxnet, pytorch, tensorflow, keras, gaussian-processes, hyperparameter-optimization, recommender-system, reinforcement-learning, jax
Last push: 2024-08-18T08:02:36+00:00

## Health v2 (maintenance only)
Score: 23/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 2886, "days_push": 745, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 29453, forks 5114 (observed 2026-08-28T04:11:53.497440+00:00)

## What it is
Dive into Deep Learning (D2L.ai) is an open-source, interactive deep learning textbook with runnable code in Jupyter notebooks, available in Chinese and English. It combines mathematical theory, critical thinking, and hands-on engineering skills, and is used as teaching material at over 500 universities in 70+ countries.

## Use cases
- learn deep learning from scratch with runnable code
- self-study machine learning and neural networks
- find a free deep learning textbook for a university course
- study computer vision and NLP fundamentals with PyTorch examples
- get hands-on practice implementing deep learning math in Python
- prepare for a career as a deep learning application scientist
- adopt an open textbook for teaching an intro to deep learning course

## When to choose
- you want an interactive, code-first introduction to deep learning
- you prefer learning by running and modifying code alongside theory
- you need a free, community-maintained resource in Chinese or English
- you are an instructor looking for proven course material with slides and forums

## When to avoid
- you need a production deep learning library or framework rather than educational material
- you want exhaustive coverage of the latest research papers rather than fundamentals
- you need a quick reference API documentation instead of a full textbook

## Facets
- artifact type: learning-resource
- maturity: active
- function: deep-learning, machine-learning, nlp, computer-vision
- domain: deep-learning, machine-learning, computer-vision, education, tutorials
- platform: python, cross-platform
- tags: open-textbook, interactive-notebooks, jupyter, chinese, self-study, university-course, natural-language-processing

## Member repositories
- d2l-ai/d2l-en (main) score 23
- d2l-ai/d2l-zh (mirror) score 23

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:11:53.497440+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 2026-08-29T16:14:03.175826+00:00, confidence not recorded.
  - readme: https://github.com/d2l-ai/d2l-en (fetched 2026-08-28T04:11:53.497440+00:00, sha 5cf4c3b17e27)
- Data as of 2026-08-30T08:39:29.467469+00:00.