PS if you want to read more of my writing, subscribe to my Substack.
I recently gave a talk at Amii about the history of GPT models.
If you want to read more of my writing, I have a Substack.
If you want to read more of my writing, I have a Substack. Articles will be posted simultaneously to both places.
There's a Substack version of this post, if you prefer that over my amateurish artisan HTML.
I’m going to try to write a weekly summary of the most interesting papers I’ve read that week. I’d love to hear what papers you’ve been reading, if you agree/disagree about my conclusions for each paper, and/or suggestions for what papers I should read next!
I regularly reference the “s-curve”, or sigmoid, as a metaphor for progress. Here, I explain what I mean, so that I can just link to this post.
I have a Substack if you want to be notified when I write.
I realized over the weekend that, unfortunately, I didn't know how back-propagation actually works (I just relied on JAX to do it for me).
I have a file in my home folder that contains Bash oneliners that I use regularly (I'm a huge nerd, naturally). I found most of them elsewhere online; I wrote very few of these from scratch.
I'm currently reading Softwar, a book about Oracle's rise. The book is brilliant, and it descibes at length Larry Ellison's sales process. There was a passage describing a meeting that Larry had that explains far more about enterprise sales than it should:
I've had a few people ask me how to do model selection correctly. Here's a
minimal example with sklearn in Python.
I'm working through the Algorithm Design Manual to improve the efficiency of my coding.
I've been trying to parse JSON files with C++, and I've found a distinct lack of full examples on how to do so. Specifically, I've struggled to find the proper commands to actually compile the code. For future reference (and to help any beginners out), here's a full example of how to use JSONcpp in your code (N.B. You're supposed to enter all of the following code in your terminal).
I'm working through TSA, and I noticed that some of my classmates are struggling to understand the difference between an ARIMA process, an AR process, and a MA process, not to mention seasonal version of the above.
I've been studying time series through TSA. The book presents a structured approach to time series analysis, and covers the material fairly well; I was impressed with the description of what a partial autocorrelation function (PACF) is, as the book explained it more intuitively than the lecture notes did. I did find the description of how to actually solve for the PACF a bit confusing, so I wrote my own explanation.