• Dealing with imbalanced classes

    JUN 20, 2019 — DEEP LEARNING

    In the real world, it is not unusual to encounter datasets which are have imbalanced classes. This post discusses some strategies for dealing with such situations.

  • Building LaTeX papers with Tup

    NOV 16, 2017 — MISC

    Building complex LaTeX documents can be quite painful. Thankfully, a little program called Tup can automate the build process for you.

  • Mode collapse in GANs

    JAN 18, 2017 — DEEP LEARNING

    How to address mode collapse, a commonly encountered failure case for GANs where the generator learns to produce samples with extremely low variety.

  • The GAN objective, from practice to theory and back again

    DEC 21, 2016 — DEEP LEARNING

    Deciphering the GAN objective used in practice, a detour through theory, and a practical reformulation of the GAN objective in a more general form.

  • Implementing InfoGAN: easier than it seems?

    DEC 1, 2016 — DEEP LEARNING

    A look at the objective function introduced in the InfoGAN paper, and why InfoGAN really isn't that complicated to implement.

  • Neural network implementation tips and tricks

    NOV 6, 2016 — DEEP LEARNING

    Implementing neural networks can be intimidating at the start, with a daunting number of choices to make with no real sense of which option might work best. By listing my (opinionated) defaults found from experience, I hope to provide you, dear reader, with a starting point from which you can train a successful neural network.

  • Calculating approximate logarithms

    MAY 6, 2015 — MISC

    Logarithms are surprisingly useful, but can be quite expensive to calculate. In this post we explore an interesting method for approximating the value of base 2 logarithms extremely quickly.

  • Hello world of assembly

    JAN 17, 2015 — MISC

  • Storing binary trees

    JAN 9, 2015 — MISC

  • The Two Generals' Problem

    JUL 31, 2014 — MISC

  • Hello world!

    JUL 31, 2014 — MISC

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