# llSourcell/learn_math_fast

This is the Curriculum for "How to Learn Mathematics Fast" By Siraj Raval on Youtube

Repository: https://github.com/llSourcell/learn_math_fast
Canonical: https://ross.abutalabs.com/products/learn_math_fast
Language: Python
License Family: other
Last push: 2019-10-12T10:56:12+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2997, "days_push": 2517, "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 3293, forks 599 (observed 2026-08-28T04:07:54.007229+00:00)

## What it is
A curated awesome-list style curriculum of mathematics learning resources, compiled by Siraj Raval to accompany his 'How to Learn Mathematics Fast' YouTube video. It links to books, lecture notes, and courses spanning foundations, algebra, analysis, probability, and math for machine learning.

## Use cases
- find resources to learn math fast
- self-study curriculum for mathematics
- learn the math behind machine learning and deep learning
- find books and lecture notes on linear algebra and calculus
- get a structured math roadmap for data science

## When to choose
- you want a curated index of free math learning materials
- you need math foundations for machine learning
- you prefer self-directed study from books and lecture notes

## When to avoid
- you want runnable code or interactive exercises
- you need a maintained, actively updated resource
- you want a single structured course rather than a link list

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: documentation
- domain: mathematics, tutorials, machine-learning, education
- platform: python
- tags: awesome-list, math-curriculum, self-study, lecture-notes

## Member repositories
- llSourcell/learn_math_fast (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:54.007229+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 2026-08-30T07:22:44.623047+00:00, confidence not recorded.
  - readme: https://github.com/llSourcell/learn_math_fast (fetched 2026-08-28T04:07:54.007229+00:00, sha d71f6d5208cb)
- Data as of 2026-08-30T08:39:29.467469+00:00.