- Dealing with imbalanced classes
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
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
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
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?
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
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
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
- Storing binary trees
- The Two Generals' Problem
- Hello world!