DSC 106: Data Visualization (@ UC San Diego)


IEEE VIS 2023: A Visual Introduction to Neural Networks

Amazon WebDevCon 2022: Why Interactive Articles?

IEEE VIS 2021: A Mathematical Explanation of Double Descent

PHX DataCon 2018: Tidy Machine Learning Workflows


YCombinator: CambioML

Working At Amazon: Jared Wilber

Amazon's Machine Learning University expands with MLU Explain


interactive articles

  • Neural Networks

    Learn about neural networks, the backbone of many popular algorithms today.

  • The Permutation Test

    A 'scrolly-telling' visual tutorial of statistical testing with hand-drawn SVG aesthetics.

  • The Good, the Rad, and the Gnarly

    An exploration into the music of skateboarding. Visual essay for The Pudding.

  • Linear Regression

    Interactively learn about linear regression models as they're commonly used in the context of machine learning.

  • Train, Test, And Validation Sets

    Learn why it is best practice to split your data into training, testing, and validation sets, and explore the utility of each with a live machine learning model.

  • Equality Of Odds

    Explore equality of odds, a metric used to quantify unfairness and remove bias from machine learning models.

  • Logistic Regression

    Learn how logistic regression can be used for binary classification in machine learning through an interactive example.

  • ROC & AUC

    A visual, interactive explanation of Receiver Operating Characteristic (ROC) Curves and Area Under The Curve (AUC).

  • Precision & Recall

    When it comes to evaluating classification models, accuracy is often a poor metric. This article covers two common alternatives, Precision and Recall, as well as the F1-score and Confusion Matrices.

  • The Bias Variance Tradeoff

    Understand the tradeoff between under- and over-fitting models, how it relates to bias and variance, and explore interactive examples related to LOESS and KNN.

  • Decision Trees

    An explanation of the Decision Tree algorithm in machine learning: how the tree makes its splits, the concepts of Entropy and Information Gain, and why going too deep is problematic.

  • Random Forest

    Learn how the majority vote and well-placed randomness can extend the decision tree model to one of machine learning's most widely-used algorithms, the Random Forest.

  • Double Descent: A Visual Introduction

    An introduction to the double descent phenomenon in modern machine learning: what it is, how it relates to the bias-variance tradeoff, and a theory of what lies behind.

  • Double Descent: A Mathematical Explanation

    A mathematical explanation of the double descent phenomenon, building on the cubic spline example introduced in the prior article.

  • Reinforcement Learning

    Learn about Reinforcement Learning (RL) and the exploration-exploitation dilemma with this interactive article.

  • Colors: National Geographic Covers

    The color-palettes of National Geographic covers, 1965-2018.

  • 4PLYMAG: All The Gall

    An analysis of Fred Gall's prolific skateboarding career.

  • Nested Machine Learning Workflows

    An introduction to nested machine learning workflows in R with tidyr & purrr.

  • U.C. Berkeley: Faculty Salaries

    Interactively explore the salaries of U.C. Berkeley faculty.

  • 4PLYMAG: Eric Koston

    A look and analysis the professional skateboarding career of Eric Koston.

  • Jenkem Mag

    Sometimes I do data-driven stuff for Jenkem Mag, a skateboard magazine.