Algorithms. Physics. Mathematics. Machine Learning.
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Shad Intensive. Memory. Guardrails.

Just want to share that I'm listening to this course. With a delay, but I'm trying to eat this mammoth piece by piece.

Today, in the "Memory and Guardrails" lecture, I didn't hear anything that gave me an insight I would like to share. Just common words about context length and context compaction in the memory part. In the guardrails part, they mentioned sources of danger like every user input, RAG, and API. I believe this is the usual computer security paranoia: you can't trust anyone. And it's much better to turn your computer off, drop it in liquid cement, and let it set.

The only thing that really interests me is not what I understood, but what I didn't. Surprisingly, I don't quite get this "context window" concept. Probably it's just my hallucination. If not, and if there is something interesting here, I'll share it.
TGIF. Meme

First of all, let's check the results of the poll. One subscriber promised to unsubscribe, and the other 9 voted for memes and shitposting. A naive approach would be to think that if I publish the meme, I'll lose 1 subscriber. But you can solve the proportion, and it gives -27.6 subscribers. The result is stunning, so, let's see. I expect to drop to 248.4 subscribers.

The topic of today's Friday meme is The Soup.

First of all. I stole these memes from The Wizard . I think that it is a magnificent channel and I ask you to promote it as widely as you can.

And now to the chase.

Dad's soup

My dad cooks absolutely hellish food.
It’s a sort of averaged recipe, because there are lots of variations.
He takes soup — but reheating it is not my dad’s style.
He pours the soup into a frying pan and starts frying it.
He adds a huge amount of onion, garlic, tomato paste, flour for thickness, and mayonnaise on top.
The whole thing fries until smoke starts coming out.
Then he takes it off the heat, lets it cool on the balcony, brings it back, pours on even more mayonnaise, and starts eating.
He eats straight from the pan, scraping it with a spoon, muttering under his breath, “oh, damn.”
Sweat is standing on his forehead.
Sometimes he politely offers me some, but I refuse.
Needless to say, the aftermath is monstrous.
The stench is so intense that the wallpaper peels off the walls.


P.S. To be honest, all this subscribe/unsubscribe stuff is starting to get to me. I’d really appreciate some support — even a couple of emojis wouldn’t hurt.

P.P.S. Tomorrow I’ll try to pull myself together and write something clever. Probably continue the “Titanic” line.
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Titanic: family bonds

Nowadays, it is hard to imagine an ML person without a Jupyter notebook. So let’s think a little outside the box and consider other options.

Quite recently, I discovered an interesting option for CSV table analysis. It consists of two instruments: DBeaver and DuckDB. The former is a Swiss army knife for database access, and the latter is a plugin that is quite powerful, or so I was told, but can also be used to open CSV files.

So, you can create a new DuckDB connection in DBeaver (ducks and beavers, yeah...), select the titanic.csv file you got from Kaggle, and start running queries.

SibSp is a factor, so to speak, horizontal in the ancestry tree. It is the sum of siblings and spouses. Parch is vertical in these coordinates: parents and children.

The first two queries show us the strength of these factors separately. The third uses the nested query technique to produce a derived factor, family size. One can see that these factors are useful, but the derived factor gives a stronger depletion/enrichment effect. This time, we were lucky enough to engineer a new feature.

I would say that family sizes 2, 3, and 4 vote for survival; the others vote against it.
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This is an illustration for the previous post.
Let’s rock ROC

All this time, it bothered me that the ROC in the post contains only three points. I’ve been thinking about this situation for a while, and I think I’ve found a small but genuinely new idea. I’m going to explain it slowly in a series of upcoming posts. But first, I want to check whether this thought is actually trivial. Please vote in the poll below, and feel free to leave comments.

Quick recap:
We have a model that gives scores of -1, 0, and 1. When we plot the ROC curve, we get a graph with 4 points and an AUROC of 56%.

The question is: can we calculate the dispersion of AUROC in this case?