Hacker News Reader

Comment by rottc0dd | Hacker News Reader

If anyone wants to delve into machine learning, one of the superb resources I have found is, Stanfords "Probability for computer scientists"(https://www.youtube.com/watch?v=2MuDZIAzBMY&list=PLoROMvodv4...).

It delves into theoretical underpinnings of probability theory and ML, IMO better than any other course I have seen. (Yeah, Andrew Ng is legendary, but his course demands some mathematical familarity with linear algebra topics)

And of course, for deep learning, 3b1b is great for getting some visual introduction (https://www.youtube.com/watch?v=aircAruvnKk&list=PLZHQObOWTQ...).

View on HN

I watched the 3b1b series on neural nets years ago, and it still accounts for 95% of my understanding of AI in general.

I’m not an ML person, but still. That guy has a serious gift for explaining stuff.

His video on the uncertainty principle explained stuff to me that my entire undergrad education failed to!

https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_6700...

It helps that 3b1b doesn't start with a curriculum and then has to figure out how to teach it. Instead he can select topics to suit his style.

> That guy has a serious gift for explaining stuff

I'd like to challenge this idea.

I don't believe he's more gifted than other people. I strongly believe that the point is he spent a lot of time and effort to get better at explaining stuff.

He contemplated feedback and improved his explanations throughout the years.

His videos are excellent because he poured himself into making them excellent, not because he has a gift.

In my experience the professors who lack this ability do so because they don't put enough effort into it, not because they were born without it.

To be very good at something it is necessary, but not sufficient, to have a talent for it. The other 85% is hard work. You aren't going to pull just anyone off the street and have the same level of instruction, no matter how motivated they are.

You're probably reading too much into previous poster's choice of the word "gift".

Most likely it is a slightly misused idiom rather than intending to convey that the teaching ability was obtained without effort.

it could one or the other or be both,

gifted and spending time to get it right are not mutually exclusive

I think real genius is translating all the heavy symbolic manipulation into visual processes, that people can see and interpret. Suddenly, you are not seeing some abstract derivation somewhat removed from real world, but another real visual process which you pause and reason with.

That makes the whole concept tick.

I disagree. He has always been excellent from the beginning of his Youtube career. Maximum potential skill levels and skill acquisition/growth rates vary from person to person. I think most people wouldn't have as much success even with twice as many hours invested in the 4 separate crafts (!) of mathematics communication, data visualization, video animation, and video editing. I know I wouldn't, and I consider technical communication one of my strong suits.

Everyone can improve with practice, but some people really are gifted.

From, a comment I posted elsewhere for written versions.

There is a course reader for CS109 [1]. You can download pdf version of this.

There is also book[2] for excellent caltech course[3].

[1] https://chrispiech.github.io/probabilityForComputerScientist...

[2] https://www.amazon.com/Learning-Data-Yaser-S-Abu-Mostafa/dp/...

[3] https://work.caltech.edu/telecourse

Your first two links don't work

That's because they posted them somewhere else (easy mistake to make.. HN doesn't show you the full link in a comment, so copy/paste just copies the ellipsis)

https://chrispiech.github.io/probabilityForComputerScientist...

https://www.amazon.com/Learning-Data-Yaser-S-Abu-Mostafa/dp/...

Thanks. Sorry for the oversight.

Caltech's learning from data was really good too, if someone is looking for theoretical understanding of ML topics.

https://work.caltech.edu/telecourse

I highly recommend the course you've mentioned (by Yaser Abu-Mostafa). In fact I still recommend it for picking up the basics; very good mix of math and intuition, Abu-Mostafa himself is a terrific teacher, and he is considerate and thoughtful in responding to questions at the end of his presentations. The last part is important if you're a beginner: it builds confidence in you that its probably ok to ask what you might consider a simple question - it still deserves a good answer. The series is a bit dated now in terms of what it covers, but still solid as a foundational course.

Apparently the word “delve” is the biggest indicator of the use of ChatGPT according to Paul Graham.

I’d love to see an article delve into why that is.

Because it's common in Nigerian English, which is where they outsourced a lot of the RLHF conditioning work to.

Really!? Do you have a source for this? This would be really interesting if true.

https://www.theguardian.com/technology/2024/apr/16/techscape...

https://pshapira.net/2024/03/31/delving-into-delve/

Saying that kind of stuff is the biggest indicator of Paul Graham (pg) himself

That seems utterly bizarre to me. I don't use "delve" frequently myself, but it is common enough that it doesn't jump out as an unusual word. Perhaps it is overused or used in a not-exactly-usual context that tips one off that it is LLM-generated, but by itself it signifies nothing to me.

It is a very common word used in Nigerian style English which was a very common place they were outsourcing RLHF tasks to. A sibling comment has a link but it is also easy to google.

As a non native speaker, I didn't know the word "delve" but now I know this word. I think internet community is learning from LLM?

  > learning from LLM

Or from each other?

So ChatGPT or Nigerians or me apparently... :`(

Non native speaker here. Will remember this.

Hm... Saw that, I have used it multiple times in my comment. I was just trying to convey the meaning.

What is right use of word? What would be right word to use here?

Native English speaker here. It was the right word. At the same time, while “delve” is common enough to be recognized, it’s not that commonly used in American English, so I also was wondering if this was AI generated.

Got it. What is the common phrase used in this case? Same as what sibling comment has said?

Absolutely, here’s why.

Nonsense. Chatgpt uses the word a lot precisely because people used it a lot.

Apparently this depends on where people are. It is not used a lot in US English, but it is used a lot in African English.

Part of training LLMs involves extensive human feedback, and many LLM makers outsource that to Africa to save money. The LLMs then pick up and use African English.

See the link in this comment [1] for an interesting article about this.

[1] https://news.ycombinator.com/item?id=43394220

It does kind of go with "deep" though when Deep Learning is the topic. Delve into the depths.

For me it’s “eerie” it just will not stop using this word.

Just watched the whole thing. Thanks! I can't get in to my Masters CS: AI program at UC Berkeley because I'm dumb, but seeing this 1st day of a Probability class kinda felt like I was beginning that program haha.

I will add a great find for starting one's AI journey https://www.youtube.com/watch?v=_xIwjmCH6D4 . Kind of needs one to know intermediate CS since 1st step is "learn Python".

and if anyone is interested in delving more deeply into the statistical concepts & results referenced in the paper of this post (e.g. VC-dimension, PAC-learning, etc), I can recommend this book: https://amzn.eu/d/7Zwe6jw

Looks nice - are there written versions?

There is a course reader for CS109 [1]. You can download pdf version of this.

There is also book[2] for excellent caltech course[3].

[1] https://chrispiech.github.io/probabilityForComputerScientist...

[2] https://www.amazon.com/Learning-Data-Yaser-S-Abu-Mostafa/dp/...

[3] https://work.caltech.edu/telecourse

Thanks!

Yeah I took CS109 (through SCPD), it was a blast. But it took some serious time commitment.

Great recommendations

Fully agree! 3blue1brown is who have single-handedly thought me a majority of what I've needed to know about it.

I actually started building my own neural network framework last week in C++! It's a great way to delve into the details of how they work. It currently supports only dense MLP's, but does so quite well, and work is underway for convolutional layers and pooling layers on a separate branch.

https://github.com/perkele1989/prkl-ann

Reply on news.ycombinator.com