The following poem, created using Google Gemini AI :robot:, chronicles my journey into the world of ergonomic DIY keyboards. It documents my (evolving) preferences and opinions, as well as a curated list of resources that I have found useful over the last three years. :keyboard: Warning :keyboard: The following verses are likely entirely incomprehensible for normal people, and could be interpreted…
tldr: I used GPT-4 Turbo, GPT-3.5 Turbo, and two open-source offline LLMs to create flashcards for a spaced repetition system (Anki) on a mathematical topic; I rated the 100 LLM-suggested flashcards (i.e., question-answer pairs) along the dimensions of truthfulness, self-containment, atomicity, whether the question-answer makes sense as a flashcard, and whether I would include a similar flashcard…
It is New Year’s Day 2023 :sweat_smile:. Happy New Year!!! :fireworks: I am currently driving with my family coast-to-coast on a road trip through the United States, but for New Year’s Eve and New Year’s Day we stayed in one place. Taking advantage of the driving free days, I and my 4-year old son had some great fun with the open-source stable diffusion models; in particular, the Text-Guided Image…
If we collect \(n\) independent pairs of observations \((y_{11}, y_{12}), (y_{21}, y_{22}), \dots, (y_{n1}, y_{n2})\) from some bivariate distribution, then how can we estimate the expected squared perpendicular distance of each such point in the 2D plane from the 45-degree line?
I can’t count how many times I have looked up the formal (measure theoretic) definitions of conditional probability distribution or conditional expectation (even though it’s not that hard :weary:) Another such occasion was yesterday. This time I took some notes.
For a while now I wanted to migrate my websites away from Github pages. While Github provides an excellent free service, there are some limitations to its capabilities, and the longer I wait the harder (or the more inconvenient) it becomes to migrate away from gh-pages. AWS S3 + CloudFront is a widely-used alternative that has been around for a long time. Moreover, I was planning to get more…
Last month, after mentioning “deep learning” a few times to some professors, I suddenly found myself in a position where I had to prepare three talks about “deep learning” within just one month… :sweat_smile: This is not to complain. I actually strongly enjoy studying the relevant theory, applying it to interesting datasets, and presenting what I have learned. Besides, teaching may be the best way…
Unfortunately this was not taught in any of my statistics or data analysis classes at university (wtf it so needs to be :scream_cat:). So it took me some time until I learned that the AUC has a nice probabilistic meaning.
The United States Patent and Trademark office (USPTO) provides immense amounts of data (the data I used are in the form of XML files). After coming across these datasets, I thought that it would be a good idea to explore where and how my areas of interest fall into the intellectual property space; my areas of interest being machine learning (ML), data science, and artificial intelligence (AI).
Recently I came across the classical 1983 paper A note on screening regression equations by David Freedman. Freedman shows in an impressive way the dangers of data reuse in statistical analyses. The potentially dangerous scenarios include those where the results of one statistical procedure performed on the data are fed into another procedure performed on the same data. As a concrete example…