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

Kaggle Competitions

I’ve been a data scientist on the following Kaggle competitions:

  • NBME - Score Clinical Patient Notes (02/2021)
  • TensorFlow - Help Protect the Great Barrier Reef (with Sohier Dane - 11/2021)
  • Santa 2021 - The Merry Movie Montage (11/2021)
  • Petfinder.my - Pawpularity Contest (10/2021)
  • Tabular Playground Series - September (with Walter Reade - 10/2021)

Tags

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.