Can we generate and then fit a distribution with gradient descent?
Quick exploration into regression coefficients in the presence of confounders and colliders.
Can we generate and then fit a multimodal distribution with gradient descent?
I’ve built a new page for displaying forecasts for carbon intensity in the UK:
After experimenting with various project structures I’ve decided to start building my own:
In this post I will be looking at building an autoencoder to compress the MNIST dataset.
See part 1. here.
In this post I will be briefly looking at PCA as a means to compress images.
Images are a matrix of pixels, where each value corresponds to some brightness.
Image compression typically involves representing those pixels in fewer dimensions than the original.
I’ve built a new page for forecasting indoor humidity:
Our problem:
For the next 100 days, we will have 1 hour to play a video games.
We have 20 games, but have no idea which one we will enjoy the most.
How do we decide what to play each day?
We assume that the enjoyment we get from a single hour is random and comes from a beta distribution.
Each game has a different distribution.
Each hour we play of a game gives us an enjoyment value and helps build our knowledge of that game.
Short article - I was thinking you could apply convolution to solve the Game of Life logic.
So I quickly built a class to do just that:
