# orico/www.mlcompendium.com

The Machine Learning & Deep Learning Compendium was a list of references in my private & single document, which I curated in order to expand my knowledge, it is now an open knowledge-sharing project compiled using Gitbook.

Repository: https://github.com/orico/www.mlcompendium.com
Canonical: https://ross.abutalabs.com/products/wwwmlcompendiumcom
Homepage: https://www.mlcompendium.com
License Family: other
Topics: machine-learning, deep-learning, algorithms, product-management, ux-design, ux-research, ux-experience, marketing, full-stack, statistics, probability, gitbook, data-science, mlcompendium
Last push: 2025-06-19T16:59:50+00:00

## Health v2 (maintenance only)
Score: 44/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 27, release rhythm 35, longevity 100
- inputs: {"age_days": 1850, "days_push": 440, "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 2195, forks 237 (observed 2026-08-28T04:06:25.158467+00:00)

## What it is
An open, Gitbook-based compendium of ~500 curated topics covering machine learning, deep learning, NLP, computer vision, time series, and data science management. It aggregates summaries, links, and articles as a free educational reference for learners at all levels.

## Use cases
- find learning resources for machine learning algorithms
- study deep learning fundamentals and techniques
- look up NLP and computer vision reference material
- learn data science management and team building practices
- find curated articles on statistics and probability
- explore time series and anomaly detection topics
- get a broad overview of the ML landscape before interviews

## When to choose
- you want a free, curated index of ML/DL reading material across many subfields
- you need quick summaries and links rather than runnable code
- you are a beginner or practitioner building a structured learning path
- you also care about the product, design, and management side of data science

## When to avoid
- you need executable code, tutorials with exercises, or a structured course
- you want up-to-date cutting-edge research papers rather than curated references
- you need a single authoritative textbook-style treatment of one topic

## Facets
- artifact type: learning-resource
- maturity: active
- function: documentation, developer-tools
- domain: machine-learning, deep-learning, data-science, computer-vision, tutorials, awesome-lists
- platform: cross-platform
- tags: gitbook, curated-list, reference, education, knowledge-base, statistics, product-management, ux-design, natural-language-processing, web-server

## Member repositories
- orico/www.mlcompendium.com (main) score 44

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:25.158467+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 2026-08-30T02:47:15.404999+00:00, confidence not recorded.
  - readme: https://github.com/orico/www.mlcompendium.com (fetched 2026-08-28T04:06:25.158467+00:00, sha c4a6e2533f4d)
  - homepage: https://www.mlcompendium.com (fetched 2026-08-29T10:28:02.024533+00:00, sha 595bee08cd4a)
  - site_page: https://www.mlcompendium.com/validation-and-evaluation/features (fetched 2026-08-29T10:28:02.034039+00:00, sha bad628248bc2)
- Data as of 2026-08-30T08:39:29.467469+00:00.