I tuned the learning rate (The magnitude of how much it changes the weights, which is multiplied by the gradient of each weight to get the final change), added better printouts when training (Added a neat progress bar/tracking, output the loss, and a timer), and also using plotmatlib to plot the embeddings. The graph is how the model arranged each letter in space. What’s interesting is similar letters (like the ending character “.” and “e”, which both are commonly found near the end of the word) are in similar spots, so the model has sorted out some sort of meaning from their placements, which I think is pretty cool. The names are still quite nonsensicle, I think I might have to enlarge the model a little bit to improve performance, which means waiting longer for it to train :(