I remember a time when AI was a dirty word. Now it's a dirty word with a lot of money behind it. Some of the stuff here is from when AI was still a dirty word, some is from later. Some is from in between when LLMs started to work but were still quite bad.
I wrote a book for O'Reilly about Machine Learning. It will teach you a wide range of techniques, using projects similar to the sort of thing that you can find on this website. Word2Vec, GANs, music analysis. You should buy it! Oh and all the code is on GitHub.
Start from a simple square and follow different evolutionary paths to create a complex animation. Each step the AI will create a new animation based on the previous one and your prompt.
Generated using stable diffusion, here we have the states and DC each represented by an image of their state animals. Not all states have state animals, sometimes it is just the fish or the state bird
Based on OpenAIs DALL-E-2, The AI not taken generates illustrations for well known poems. For each poem there is an associated artist (usually from the same time, maybe same style) that is used to create an image in that style matching each line of the poem.
Inspired by Gap Minder, this shows the population of the countries of the world by morphing their shapes from 1880 to 2020.
Gramzoom uses the key element of the Style Tranfer Algorithm to create an infinitely zoomable movie from any image. It works best with images that are self similar, but anything will really do. Can cats look evil? It might just break the Internet.
Using Recurrent Neural Networks to generate Icons and Hieroglyphs. The icons are encoded in a machine learnable format.
An implementation of a Spotify-like song radio based on Word2Vec. Based on a large set of crawled playlists and using those playlists as sentence equivalents. The Word2Vec algorithm then produces a vector per song.
An implementation of deep dreaming where the network is restricted to just black and white and starts with a blob in the middle. It forces it to draw from the center and create ink like patterns.
World map of uses Word2Vec to color a world map based on the distance between words and the names of countries. Country names are an interesting way to geocode the semantic values of words though a bit noisy.
Create universal numbers by comparing the edit distance between all numbers from Wikitravel's phrasebooks and for each picking the 'median' one. As a side effect, create a tree an evolutionary tree.
Using the Artistic Style algorithm to restyle pictures of museums in the style of their most famous painting. This way you get immediately an idea of what to expect at these museums. Uses data from Wikipedia, Wikidata andthe Wikistats. Uses Anish Athalye Tensorflow implementation
Mostly for the 2014 World Cup, with some adjustments to make it work for the European Championship in 2016,this model takes previous matches and tries to predict the future outcomes. The model is super simple andyet especially in 2016 did rather well.
This project uses the twitter api to automatically generate tweets based on what is commonly tweeted. It listens to the general tweetstream and captures fragments. By randomly recombining those fragments something that reads almost like real tweets appears. It also gives an interesting insight into what the average user uses twitter for.