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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
The basic distribution probability Tutorial for Deep Learning Researchers
| Date | Stars |
|---|---|
| 2026-07-31 | 1640 |
| 2026-08-06 | 1640 |
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## **distribution-is-all-you-need** **distribution-is-all-you-need** is the basic distribution probability tutorial for **most common distribution focused on Deep learning** using python library. #### Overview of distribution probability  - `conjugate` means it has relationship of **conjugate distributions**. > In [Bayesian probability](https://en.wikipedia.org/wiki/Bayesian_probability) theory, if the [posterior distributions](https://en.wikipedia.org/wiki/Posterior_probability) *p*(*θ* | *x*) are in the same [probability distribution family](https://en.wikipedia.org/wiki/List_of_probability_distributions) as the [prior probability distribution](https://en.wikipedia.org/wiki/Prior_probability_distribution) *p*(θ), the prior and posterior are then called **conjugate distributions,** and the prior is called a **conjugate prior** for the [likelihood function](https://en.wikipedia.org/wiki/Likelihood_function). > [Conjugate prior, wikipedia](https://en.wikipedia.org/wiki/Conjugate_prior) - `Multi-Class` means that Random Varivance are more than 2. - `N Times` means that we also consider prior probability P(X). - To learn more about probability, I recommend reading [pattern recognition and machine learning, Bishop 2006]. ## distribution probabilities and features 1. **Uniform distribution(continuous)**, [code](uniform.py) - Uniform distribution has same probaility value on [a, b], easy probability. <p align="center"><img width="400" src="graph/uniform.png" /></p> 2. **Bernoulli distribution(discrete)**, [code](bernoulli.py) - Bernoulli distribution is not considered about prior probability P(X). Therefore, if we optimize to the maximum likelihood, we will be vulnerable to overfitting. - We use **binary cross entropy** to classify binary classification. It has same form like taking a negative log of the bernoulli distribution. <p align="center"><img width="400" src="graph/bernoulli.png" /></p> 3. **Binomial distribution(discrete)**, [code](binomial.py) - Binomial distribution with parameters n and p is the discrete probability distribution of the number of successes in a sequence of n independent experiments. - Binomial distribution is distribution considered prior probaility by specifying the number to be picked in advance. <p align="center"><img width="400" src="graph/binomial.png" /></p> 4. **Multi-Bernoulli distribution, Categorical distribution(discrete)**, [code](categorical.py) - Multi-bernoulli called categorical distribution, is a probability expanded more than 2. - **cross entopy** has same form like taking a negative log of the Multi-Bernoulli distribution. <p align="center"><img width="400" src="graph/categorical.png" /></p> 5. **Multinomial distribution(discrete)**, [code](multinomial.py) - The multinomial distribution has the same relationship with the categorical distribution as the relationship between Bernoull and Binomial. <p align="center"><img width="400" src="graph/multinomial.png" /></p> 6. **Beta distribution(continuous)**, [code](beta.py) - Beta distribution is conjugate to the binomial and Bernoulli distributions. - Using conjucation, we can get the posterior distribution more easily using the prior distribution we know. - Uniform distiribution is same when beta distribution met special case(alpha=1, beta=1). <p align="center"><img width="400" src="graph/beta.png" /></p> 7. **Dirichlet distribution(continuous)**, [code](dirichlet.py) - Dirichlet distribution is conjugate to the MultiNomial distributions. - If k=2, it will be Beta distribution. <p align="center"><img width="400" src="graph/dirichlet.png" /></p> 8. **Gamma distribution(continuous)**, [code](gamma.py) - Gamma distribution will be beta distribution, if `Gamma(a,1) / Gamma(a,1) + Gamma(b,1)` is same with `Beta(a,b)`. - The exponential distribution and chi-squared distribution are special cases of the gamma distribution. <p align="center"><img width="400" sr
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Tae Hwan Jung · @clober-dex · South Korea
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:5eab9af54d5175ca, topic:deep-learning