# hadrienj/deepLearningBook-Notes

Notes on the Deep Learning book from Ian Goodfellow, Yoshua Bengio and Aaron Courville (2016)

Repository: https://github.com/hadrienj/deepLearningBook-Notes
Canonical: https://ross.abutalabs.com/products/deeplearningbook-notes
Homepage: https://hadrienj.github.io/posts/Deep-Learning-Book-Series-Introduction/
Language: Jupyter Notebook
License: MIT
License Family: permissive
Last push: 2020-05-08T07:55:10+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3265, "days_push": 2308, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1782, forks 562 (observed 2026-08-28T04:05:35.701213+00:00)

## What it is
A collection of Jupyter Notebook notes with Python code, illustrations, and intuitions covering the linear algebra chapter of the Deep Learning Book by Goodfellow, Bengio, and Courville. It is aimed at beginners who want to understand the math underlying machine learning and deep learning.

## Use cases
- learn linear algebra for deep learning
- understand matrices as linear transformations
- study eigendecomposition and SVD with Python code
- find beginner-friendly notes on the Deep Learning Book chapter 2
- build math foundations for data science
- visualize linear algebra concepts with plots

## When to choose
- you are a beginner or advanced beginner wanting intuitive explanations of linear algebra for ML
- you prefer learning through code, examples, and drawings rather than pure definitions
- you want a structured syllabus following the Deep Learning Book's linear algebra chapter

## When to avoid
- you need a comprehensive treatment of the entire Deep Learning Book beyond chapter 2
- you want a rigorous, formal math textbook rather than intuitive notes
- you need actively maintained or updated course material

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, math, data-science
- domain: deep-learning, machine-learning, mathematics, tutorials, data-science
- platform: python, cross-platform
- tags: jupyter-notebooks, linear-algebra, deep-learning-book, educational, svd, eigendecomposition

## Member repositories
- hadrienj/deepLearningBook-Notes (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:35.701213+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 2026-08-30T03:24:21.536417+00:00, confidence not recorded.
  - readme: https://github.com/hadrienj/deepLearningBook-Notes (fetched 2026-08-28T04:05:35.701213+00:00, sha d13017d6247c)
  - homepage: https://hadrienj.github.io/posts/Deep-Learning-Book-Series-Introduction/ (fetched 2026-08-29T11:02:55.388201+00:00, sha 860045ba6e9f)
- Data as of 2026-08-30T08:39:29.467469+00:00.