I made some major changes to this website’s content and aesthetics this week, which you may or may not have noticed. First, I modified some of the fonts that are used, and installed a new minimalistic font that I felt went a bit better with the site’s theme. That font is now used on some of the page headings and titles across the website. Second, I completely revamped the Short Stories & Poetry page to better showcase my various poems and stories in a grid layout. Each piece has its own Inspirations & Comments section, where I attempt to convey what led me to compose the piece, and the meaning behind it.
I’m still working on adding more of my poetry and short stories, but I have a semi-comprehensive list on the Short Stories & Poetry home page. In fact, I’m working on a rather unusual coming-of-age short story right now; it might be the submission for the 2027 Scholastic Art & Writing awards. Once that’s done, I’ll attempt to upload it to the website, and attach some comments. I’m contemplating adding a background-header sort of picture to the short stories & poetry page, similar to the large one on the homepage. However, I’d like the text to be overlaid upon the background image, and thus far, I haven’t found a way to do that with clunky WordPress.
I’ve also made some minor tweaks to the Musical Works page, changing the names of some tabs and linking some of the most recent compositions. As a quick note, it is now possible for people to fill out the site’s Contact form to obtain copies of the scores; there is a one-page preview image available for some of the more recent pieces and a button to get to the Contact form. I don’t plan on charging money for the scores; this is just a mechanism to prevent bots (or people) from coming on and scamming the scores to a wide variety of unauthorized individuals.
Deep Learning Course: Finally, Some Progress
After about a week of screaming and coming close to quitting the entire course, I finally started making some progress on the fast.ai Practical Deep Learning for Coders course. Yesterday, I finished training the first major project, which is a painting classifier designed to identify the artist of a given painting image. Right now, I’m training it on the works of twenty different Impressionistic artists, with some more modern painters also included. I’m amazed at how much easier it is to use PyTorch to create a classifier than Tensorflow; all you need to do is pull down your images, create a DataBlock, and initiate your learner.
The reason why I was getting so blocked on this model before was the stupid DuckDuckGo search issues, combined with issues with Kaggle. This sort of classifier relies on pulling down batch images from the DuckDuckGo search API, which is flaky at best and catastrophically time-consuming at worst. So why am I using DuckDuckGo, of all search APIs? Because Microsoft has gone bad and locked their Bing Search API behind a paywall. In a similar way, Google’s search system requires several different API keys, extensive verification, and massive amounts of credentials that make no sense for a small project like this one.
At first, I thought I was going to have to quit out of this project and find a different data setup that didn’t rely on DuckDuckGo. But then, on Monday morning, I “magically” stopped getting 403 Ratelimit errors, and started downloading images as normal. So I jumped on the opportunity and immediately trained the model to completion with all 4,000 images; after doing some brief testing, it looked like the model was actually pretty good. I’m going to obtain some empirical data on the model’s performance–accuracy, loss, and others–shortly, and use that data to experiment with some different model architectures and data configurations.
I also did some minor refactoring of the model’s code, which is now available in my official fast.ai course repository on GitHub (though I don’t recommend doing anything with it yet, as it’s still very preliminary, and the repo is still a mess). In unit 2 of the fast.ai deep learning course, I aim to release this painting classifier via a web API so you can interact with it directly; when that’s done I won’t have to deal with muddled GitHub repositories, local files, and broken Kaggle runtimes because the model is released into production already.
Well, that concludes this regular site update. Stay tuned for further news on AI projects and creative pieces!
