# graykode/distribution-is-all-you-need

The basic distribution probability Tutorial for Deep Learning Researchers

Repository: https://github.com/graykode/distribution-is-all-you-need
Canonical: https://ross.abutalabs.com/products/distribution-is-all-you-need
Language: Python
License: MIT
License Family: permissive
Topics: deep-learning, mathmatics, probability, gaussian, distribution
Last push: 2020-10-01T19:14:25+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": 2553, "days_push": 2162, "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 1639, forks 378 (observed 2026-08-28T04:05:15.199981+00:00)

## What it is
A Python-based tutorial repository explaining the most common probability distributions (uniform, Bernoulli, binomial, categorical, multinomial, Gaussian, etc.) with visualizations and code examples, aimed at deep learning researchers. It connects each distribution to concepts like conjugate priors and cross-entropy loss.

## Use cases
- learn probability distributions for deep learning
- understand the math behind cross-entropy loss
- see visualizations of common probability distributions
- review conjugate prior relationships in Bayesian statistics
- find Python code examples for sampling distributions
- prepare for machine learning interviews on probability

## When to choose
- you are a deep learning researcher wanting an intuitive overview of probability distributions
- you want short, runnable Python examples with plots for each distribution
- you need a quick refresher connecting distributions to loss functions like binary cross-entropy

## When to avoid
- you need a rigorous, comprehensive statistics textbook treatment
- you want production code for probabilistic modeling rather than educational snippets
- you need actively maintained content with updates and support

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, math, data-science
- domain: deep-learning, machine-learning, mathematics, tutorials
- platform: python
- tags: probability-distributions, statistics, tutorial, bayesian, deep-learning-theory

## Member repositories
- graykode/distribution-is-all-you-need (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:15.199981+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 2026-08-30T03:46:31.117527+00:00, confidence not recorded.
  - readme: https://github.com/graykode/distribution-is-all-you-need (fetched 2026-08-28T04:05:15.199981+00:00, sha 06905bb9f8c7)
- Data as of 2026-08-30T08:39:29.467469+00:00.