Math for Machines
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Recent Posts

  • Visualizing What Convnets Learn
  • Visualizing the Loss Landscape of a Neural Network
  • Getting Started with TPUs on Kaggle
  • Six Varieties of Gaussian Discriminant Analysis
  • Optimal Decision Boundaries
  • Least Squares with the Moore-Penrose Inverse
  • Understanding Eigenvalues and Singular Values
  • Visualizing Linear Transformations
  • What I'm Reading 1: Bayes and Means
  • investmentsim - an R Package for Simulating Investment Portfolios

About

This is the website of Ryan Holbrook. You can contact me at ryan@mathformachines.com.

I am a data scientist at Kaggle. You can also find me on Twitter and GitHub.

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bayesian BMA calculator category-theory classification convnets coordinates covectors cql data-science decision-boundaries deep-learning eigenvalues engrams finance functional-programming generalized-inverse geometry haskell investing julia kaggle LDA least-squares linear-algebra linear-equations matrix-decomposition MCMC memory moore-penrose-inverse neural-networks neuroscience NLP numpy python QDA R ReLUs retirement review sage sgd simulation singular-values stacking talk tensorflow tensors tpus tutorial vectors visualization

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Math for Machines by Ryan Holbrook is licensed under a Creative Commons Attribution 4.0 International License.