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

Health v2 · maintenance only

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Adoption not part of the score

1639 stars · 378 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

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

learning-resource · maturity maintenance

machine-learning math data-science deep-learning machine-learning mathematics tutorials python probability-distributions statistics tutorial bayesian deep-learning-theory

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