## What's behind matrix multiplication?

A simple interpretation of a complex formula

A simple interpretation of a complex formula

How adding zero can solve hard problems

How false intuitions about probability can lead to financial ruin

You are (probably) wrong about it

How to write poetry by typing random letters

The origins of frequentist and Bayesian interpretations

The Monte Carlo method

Going from a single variable to millions

Behind Euler's formula

What's behind the most important function of all times

Reducing complexity by turning multiplication into addition

The brilliance of Newton's calculus

Explaining the single most important concept of science and engineering

How hooks can significantly improve your workflow

An introduction to weight pruning

Going from floats to integers

A guide to the beautiful world of mathematics for machine learning

A principle that reveals a deep connection between combinations, variations, and permutations

There is a profound lesson even in some of the simplest mathematical problems: a good representation is half the success

The principles of combinatorics

Matrix factorizations, the pinnacle of linear algebra

The single most important concept in probability and statistics

Probability: the logic of science

The single most undervalued fact of linear algebra: matrices are graphs, and graphs are matrices

Taking a walk can change the course of science

Recursion (almost) at the speed of light!

One operation, three different perspectives

This is why the house always wins

This is why our intuition are deceived by probability

If you haven't tried FastAPI yet, it is time

An introduction to knowledge distillation

An introduction to weight pruning, quantization, and knowledge distillation

Practical problem solving strategies for the working data scientist

The geometric explanation behind cosine similarity

A measure-theoretic introduction

A brief guide about how to minimize a function with millions of variables

A look beyond function fitting

Looking behind the curtain of one of the most influential dimensionality reduction algorithms

Understanding the inner workings of neural networks from the ground-up

How to update our models given new observations

Looking behind one of the most commonly used loss functions

The universal approximation theorem

An in-depth explanation of principal component analysis

The building blocks of describing and manipulating data

What is behind the not-so-simple formula?

A simple explanation