It says its tailored for beginners, but I don't know what kind of beginner can parse multiple paragraphs like this:
"How wrong was the prediction? We need a single number that captures "the model thought the correct answer was unlikely." If the model assigns probability 0.9 to the correct next token, the loss is low (0.1). If it assigns probability 0.01, the loss is high (4.6). The formula is
−
log
(
�
)
−log(p) where
�
p is the probability the model assigned to the correct token. This is called cross-entropy loss."
politelemon [3 hidden]5 mins ago
> By the end of training, the model produces names like "kamon", "karai", "anna", and "anton". None of them are copies from the dataset.
You are absolutely right. The whole post reads like AI generated.
jsheard [3 hidden]5 mins ago
The rate they are posting new articles on random subjects is also a pretty indicative of a content mill.
In 3 days they've covered machine learning, geometry, cryptography, file formats and directory services.
re [3 hidden]5 mins ago
I didn't get that sense from the prose; it didn't have the usual LLM hallmarks to me, though I'm not enough of an expert in the space to pick up on inaccuracies/hallucinations.
The "TRAINING" visualization does seem synthetic though, the graph is a bit too "perfect" and it's odd that the generated names don't update for every step.
I read through this entire article. There was some value in it, but I found it to be very "draw the rest of the owl". It read like introductions to conceptual elements or even proper segues had been edited out. That said, I appreciated the interactive components.
davidw [3 hidden]5 mins ago
It started off nicely but before long you get
"The MLP (multilayer perceptron) is a two-layer feed-forward network: project up to 64 dimensions, apply ReLU (zero out negatives), project back to 16"
Which starts to feel pretty owly indeed.
I think the whole thing could be expanded to cover some more of it in greater depth.
The part that eludes me is how you get from this to the capability to debug arbitrary coding problems. How does statistical inference become reasoning?
For a long time, it seemed the answer was it doesn't. But now, using Claude code daily, it seems it does.
HN.zip
"How wrong was the prediction? We need a single number that captures "the model thought the correct answer was unlikely." If the model assigns probability 0.9 to the correct next token, the loss is low (0.1). If it assigns probability 0.01, the loss is high (4.6). The formula is − log ( � ) −log(p) where � p is the probability the model assigned to the correct token. This is called cross-entropy loss."
Hey, I am able to see kamon, karai, anna, and anton in the dataset, it'd be worth using some other names: https://raw.githubusercontent.com/karpathy/makemore/988aa59/...
In 3 days they've covered machine learning, geometry, cryptography, file formats and directory services.
The "TRAINING" visualization does seem synthetic though, the graph is a bit too "perfect" and it's odd that the generated names don't update for every step.
"The MLP (multilayer perceptron) is a two-layer feed-forward network: project up to 64 dimensions, apply ReLU (zero out negatives), project back to 16"
Which starts to feel pretty owly indeed.
I think the whole thing could be expanded to cover some more of it in greater depth.
Microgpt
https://news.ycombinator.com/item?id=47202708
For a long time, it seemed the answer was it doesn't. But now, using Claude code daily, it seems it does.