I’ve reached some ML milestones that I want to write about, not because they are impressive (it’s really just basic material) but because I like to think eventually it could be interesting to read back to this date and see what I was up to around this time.

I finally finished MLCC “for real”; I had left some exercises (MNIST and embeddings) pending due to a rush to complete it in time for another course that listed it as a prerequisite. I tried going through the exercises without taking shortcuts, and experimenting with enough variations on hyperparameters and reading the code/API documentation, so it took me a few more hours than I thought it would. I enjoyed MLCC but it’s nice to be 100% done with it (I’m known for leaving lots of things unfinished).

I also finished listening to OCDevel’s Machine Learning guide. It was one of many resources I got from Alexis Sanders’ ML guide for average humans, most of which are still in my to do list. I liked OCDevel’s podcast because it gives a high level overview of a lot of concepts, and I found it worked well as both introduction and review (about half of the episodes were about topics I already had studied/read about elsewhere). It also allowed me to relatively easily hit this soft target I have of “ML every day”: basically to do something related to ML every day, even if it’s only five minutes long. I took the same approach two years ago when I started playing the piano, and it’s been working for me; I’ll never be a good pianist, but honestly I sometimes feel rather good about the kind of pieces I can play today, and just the process of learning has been lots of fun and given me joy. If I can make ML be like this I think I may come to understand at least some slice of the field reasonably well eventually.

Finally, I resumed Andrew Ng’s Coursera course. I had put it on pause around the sixth week (exercises done until the fourth) to focus on other things, but I want to go back and complete this. Once again, I’m not known for being a great finisher overall, but I want to make my ML hobby different in this respect.


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