...It explains common discrete and continuous distributions through short scripts, formulas, descriptions, and plotted graphs. Covered topics include uniform, Bernoulli, binomial, categorical, multinomial, beta, Dirichlet, gamma, exponential, Gaussian, normal, chi-squared, and Student's t distributions. The material highlights relationships such as conjugate priors and special-case distributions. Examples connect probability functions with machine learning concepts including binary and multiclass cross-entropy. An overview image and presentation summarize the distribution families and their connections for quick reference.