120 cards
This deck walks you through the foundational probability distributions you'll encounter in machine learning, starting with the basics of what a probability distribution actually is and how discrete and continuous variables are handled differently. You'll get clear definitions of core tools like the probability mass function, probability density function, and cumulative distribution function, along with how they relate to one another. From there, it moves into summary statistics such as expected value, variance, and standard deviation, before introducing the specific distributions most relevant to ML: Bernoulli, Binomial, Categorical, and Multinomial.
It's a great fit if you're a machine learning learner, data science student, or practitioner who wants to strengthen the mathematical foundation underneath algorithms like logistic regression, naive Bayes, or generative models. If you've ever felt shaky about why a sigmoid outputs a probability, or what "sampling from a distribution" really means, working through these cards will make those ideas much more intuitive.
Because the topics build on each other, going through the deck in order the first time can help you see the connections between distributions rather than treating them as isolated formulas. After that, spaced repetition works really well here: review the cards over several days or weeks rather than cramming, and try to explain each concept in your own words before flipping the card. Linking each distribution back to a real ML use case, even a simple one, will also help the material stick far longer than memorizing formulas alone.