Summary: Describe the process of creating what is likely the first ever machine translation model for the Hani language, starting with no previous datasets or trained models. Describe data, tools, techniques, and commands used, hopefully enabling easier progress in low-resource translation efforts like this one. Present a baseline and ideas for future improvement.

Brief thoughts and takeaways from the Association for Computational Linguistics 2020 conference, especially as the relate to Bible Translation. Not likely to be complete or even accurate. If I’ve gotten anything wrong feel free to let me know and I’ll try to correct it.

Previously, I discussed why I’m curious about machine learning and Bible translation. Here, I will be collecting resources I find interesting or relevant. I hope to grow and improve this page over time.

So, I’ve now begun a PhD in machine translation at the University of Dayton. I have it on good authority that blogging about the process can be of great help. This is the start of that! As usual, the idea is for it to be a stable, long-term essay that gets better over time.

Very quick post: NVIDIA makes the best deep learning toys. Latest one is GANimal (formerly Petswap), which lets you upload a picture of your pet, and transform it to look like other animals. So I uploaded a picture of my face instead: Me, transformed to many 16 different animals

This Is A Sidequest / That Uses GPT2 / To Generate Haikus…

Hi, and welcome back to Deeply Curious! We’re still goofing around in Latent Space. Last time, I promised we’d “go way too deep into age, gender, and smileyness… and then keep going!” That’s still coming, but first, we must find fill a table, with truth!

(A video of the StyleGAN encoder in action. In this case, within 11,000 iterations, StyleGAN has pretty much nailed my face.)

Hi, I’m Colin, and welcome to Deeply Curious, where I have way too much fun playing with deep learning and AI.