alper111

The librarian's name is Elf.

I was wondering the same; whether the comments were produced by chatgpt.

Plain MLP acc. is 63% vs 59% with FF, not so bad? By the same logic, MLP is a failed toy model.

This looks very good, thanks.

I was one of those people and now will give it a new try.

Hmm... Such a strong password requirement for a casual app. No google signup integration. clicks on the user and see the submission "Check username availability ..." ookay. Thx.

This seems very desirable. Though at the moment, when the self-attention got popular for the first time, it was already available in PyTorch. Python seems to have the edge just because there are lots of people using it. Maybe it is just a matter of time and users. Probably I will wait until the ecosystem gets larger, and then switch to it. (Yes, I am a lazy person to implement a transformer from scratch)

I also do not understand the downvote for a fair criticism.

It is quite interesting that LeCun is very critical when it comes to GPT from OpenAI. The same arguments can also be said for the current deep learning paradigm and convolutional nets, but you don't see any criticism from him when it comes to this stuff. Look at his arguments when he is tweet-debating with Gary Marcus.

Yes, I agree with you. My comment looks dull with your explanation. Of course, I also don't advocate doing something without any hypothesis. Though, I think hands-on practice on a subject can help you learn things better. For example, I find it very useful for linear algebra (maybe this is not applicable to other subjects). I can analyze my flawed intuitions.

When I first heard about the Monty Hall problem, I didn't understand it and tried it myself. It was way easier for me to understand the flawed intuition by analyzing each line as opposed to, say, for example, the explanation of Judea Pearl (which is also good).

What I wanted to emphasize is that it is not bad guessing your way through a problem you don't understand. But yeah, of course, you should have some knowledge.

> is there any worse feeling that guessing your way through a problem you don't understand?

Isn't this called science?

If you look at network structure, it acts as one agent, not five. So, free coordination. (See: https://t.co/GPKHPsIu1C)

In my opinion, what i see is a very good player who knows how to chain stun precisely without any strategic depth. If you claim to have built an AI system, which you ultimately want it to evolve to AGI, you at least expect some sort of strategic decision making at the macro level. Though since it has almost perfect micro, it can easily outweight the most of teams. So yeah, with that expectation I see this as a joke, too.

P.S. The model is trained with 128k cpus and 256 gpu. It is able to play 180 years worth of game in a day. Think about it.

I agree that probably working on bash feels more snappy but Jupyter is not that clunky as you mentioned. My laptop is a mediocre one and I can run the Jupyter, navigate to the code and open the editor in total like 3 seconds.

In my experience, kernels work just fine unless you are doing something you shouldn't. My kernel problems occur at points where I have a memory issues. Though I agree with you that you don't understand what's wrong once the kernel dies.