# nivu/ai_all_resources

A curated list of Best Artificial Intelligence Resources

Repository: https://github.com/nivu/ai_all_resources
Canonical: https://ross.abutalabs.com/products/ai_all_resources
Language: Jupyter Notebook
License Family: other
Topics: machine-learning, regression, neural-networks, tensorflow, data-science, decision-trees, deep-learning, mathematics, artificial-intelligence, rnn, gan, reinforcement-learning, convolutional-neural-networks, support-vector-machine, random-forest, knn, kmeans, statquest, python, statistics
Last push: 2026-04-03T10:49:05+00:00

## Health v2 (maintenance only)
Score: 66/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 75, release rhythm 35, longevity 100
- inputs: {"age_days": 2442, "days_push": 152, "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 1520, forks 313 (observed 2026-08-28T04:04:57.675686+00:00)

## What it is
A curated list of the best artificial intelligence, machine learning, and deep learning learning resources, including tutorials, blogs, and videos from well-known educators. It covers mathematics, statistics, and core ML/DL algorithms with links for getting started in data science.

## Use cases
- find tutorials to learn machine learning from scratch
- best resources to learn deep learning and neural networks
- how do I get started with a data science career
- learn the math and statistics needed for machine learning
- curated list of AI blogs and YouTube channels
- learn reinforcement learning, GANs, or CNNs
- beginner-friendly introduction to machine learning algorithms

## When to choose
- you want a single curated starting point for learning AI/ML/DL
- you prefer free tutorials, videos, and blogs from trusted educators
- you need math and statistics foundations before diving into ML

## When to avoid
- you need runnable code, a library, or a framework rather than links
- you want structured courses with certificates or hands-on grading
- you need up-to-date coverage of the newest LLM tooling

## Facets
- artifact type: learning-resource
- maturity: active
- function: machine-learning, deep-learning, data-science, nlp
- domain: artificial-intelligence, machine-learning, deep-learning, data-science, tutorials, awesome-lists
- platform: python
- tags: curated-list, tutorials, mathematics, statistics, neural-networks, reinforcement-learning, tensorflow, statquest

## Member repositories
- nivu/ai_all_resources (main) score 66

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:57.675686+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 2026-08-30T04:31:55.293891+00:00, confidence not recorded.
  - readme: https://github.com/nivu/ai_all_resources (fetched 2026-08-28T04:04:57.675686+00:00, sha 02638a764170)
- Data as of 2026-08-30T08:39:29.467469+00:00.