Live data from Hacker News

Stanford A.I. Courses

ai.stanford.edu

11–20 of 81 posts

Re: Stanford A.I. Courses

#11

Looking for guidance here. There are a lot of courses out there on AI from esteemed institutions at that. What do people recommend as a curriculum for someone with a formal univ education in CS albeit from a while ago and who has programmed extensively though not in Python. The goal at the end is to have a deep understanding of the LLM space and its adjacencies.

Define “deep understanding” here? You certainly have to lean python, at least because you are gonna need it for data manipulation and cleaning no matter what you do in this field.

Re: Stanford A.I. Courses

#12

Looking for guidance here. There are a lot of courses out there on AI from esteemed institutions at that. What do people recommend as a curriculum for someone with a formal univ education in CS albeit from a while ago and who has programmed extensively though not in Python. The goal at the end is to have a deep understanding of the LLM space and its adjacencies.

Although I myself am not related to the industry or academia pertaining to AI, I have heard many people speak highly of the zero to hero course by Andrej Karpathy: https://youtube.com/playlist?list=PLAqhIrjkxbuWI23v9cThsA9Gv...

I myself loved it and learned a lot, but YMMV

Re: Stanford A.I. Courses

#13

Looking for guidance here. There are a lot of courses out there on AI from esteemed institutions at that. What do people recommend as a curriculum for someone with a formal univ education in CS albeit from a while ago and who has programmed extensively though not in Python. The goal at the end is to have a deep understanding of the LLM space and its adjacencies.

How long has it been since you studied/used university-level math? Calculus and linear algebra in particular.

I ask because it’s pretty difficult to get through the math of backprop without a firm grasp of these. The Python part is trivial by comparison, the main difficulty being the matching of dimensions.

Re: Stanford A.I. Courses

#14
I think the way courses are taught can give you some needed grounding, like you should always take a good linear regression class. But I think that is as far as it gets you, a theoretical base.

Honestly the issue is that most ML programs are taught as being some kind of additive skill set: the more courses you take the better or selection of the right kind of courses gets you some where.

In reality:

1. most real world problems are also about subtraction knowing what not to try and why it might not work. Like when I ask people about Recommendtaion engines for recommending colocated things, people pile on embeddings, in reality its about finding good false negatives to train datasets, calibration of classifier output and those are really hard problem. Embeddings may be necessary but are the least of your worries.

2. Most companies will not teach you about the fundamentals of stats; you will be lucky if you can get a mentor in a company that has both the theoretical rigour and the practical implementation skill to solve problems.

3. Most ML problems require engineering to work as well, for example you can't use Bayesian MCMC to do most things at scale. Its why Topic models that used statistical models like simulating posterior were crazy expensive on large datasets.

Re: Stanford A.I. Courses

#16
post #2

Can I take these courses online for free or is this an ad for Stanford?

Some of the courses have been available for free on YouTube for quite a while: https://www.youtube.com/@stanfordonline There's also Coursera courses that are much of the same content (taught by Andrew Ng as well in many cases). They have specializations for Machine Learning [1], Deep Learning, etc. These are paid via Coursera subscription, but financial assistance is available [1] https://www.coursera.org/specializat…

I recently completed the specialization with Andrew Ng and think it’s a fantastic introduction to ML. It has a good blend of theory, practical tips, and coding.

If anyone is interested, I’ve published detailed notes and my submissions for the lab assignments:

https://github.com/pmulard/machine-learning-specialization-a...

Re: Stanford A.I. Courses

#17
post #14

I think the way courses are taught can give you some needed grounding, like you should always take a good linear regression class. But I think that is as far as it gets you, a theoretical base. Honestly the issue is that most ML programs are taught as being some kind of additive skill set: the more courses you take the better or selection of the right kind of courses gets you some where. In reality: 1. most real worl…

4. Models are taught like an end, but courses don't teach you to mix them for debugging. They are usually a means to an end for example say you are using decision trees and your models are acting up, you could still try some debugging techniques from linear regression like residual analysis or plotting variable slopes of each variable vs Y to debug before jumping for shapley values.

The reason is not that using shapley values is bad, they are great, but you can get a lot of insight by having some base models that are simpler to debug.

Re: Stanford A.I. Courses

#18
Why is Convex Optimization (EE364a) not included?

https://stanford.edu/class/ee364a/

https://www.youtube.com/playlist?list=PL3940DD956CDF0622

It's one of the best courses to take if you want to obtain some fundamental understanding of the mathematical concepts behind AI.

Yes there's much more to it than NNs/transformers/'Attention is all you need' paper/whatever else is trendy right now. No, don't expect to do important, as in employable, work if you won't be spending some time truly understanding the mathematical foundations.

Re: Stanford A.I. Courses

#19

Are there project-based tutorial that talks more about neural net architecture, hyperparameters selection and debugging? Something that walks through getting poor results and make explicit the reasoning for tweaking? When I try to use transformers or any AI thing on a toy problem I come up with, it never works. Even Fizz-Buzz which I thought was easy doesn't work (because division or modulo is apparently hard to repr…

There's no great answer to this question. It is a bunch of tricks. Fundamentally: If you're saying FizzBuzz doesn't work, presumably you mean that encoding the n directly doesn't work. Neither does encoding n from 0 to 1 or between -1 and 1 (and don't forget: obviously don't use relu with -1 to 1). It doesn't. Neural networks can do a LOT of things, but they cannot deal with numbers. And they certainly cannot deal wi…

Thanks for answering, what you wrote here is exactly the sort of thing I'm talking about. Something implicit that's known but not obvious if you look at the first few lectures of the first few courses (or blogs or announcements, etc).

You mention bag of tricks and that's indeed one issue but its worse than that because it includes knowing what "silent problems" needs a trick applied to it in the first place!

Indeed, despite using vectors everywhere, NN are bad with numerical input encoded as themselves! Its almost like the only kind of variables you can have are fixed size enums. That you then encode into vectors that are as far apart as possible, and unit vectors ("one hot vectors") do this. But that's not quite it and sometimes you can still some meaningful metric on the input that's preserved in the encoding (example: word embeddings). And so its again unclear what you can give it and what you can't.

In this toy example, I have an idea of what the shape of the solution is. But generally I do not and would not know to use a base 15 encoding or to send it the last 5 (or 15) outputs as inputs. I know you already sort of addressed this point in your last few paragraphs.

I'm still trying out toy problems at the time so it might be a "waste" of your time to troubleshoot these but I'm happy to take you up on the offer. HN doesn't have PMs though.

Do you remember when you first learned about the things you are using in your reply here? Was it in a course or just asking someone else who worked on NN for longer? I learned through by googling and finding comment threads like these! But they are not easy to collect or find together.

Re: Stanford A.I. Courses

#20
post #13

Looking for guidance here. There are a lot of courses out there on AI from esteemed institutions at that. What do people recommend as a curriculum for someone with a formal univ education in CS albeit from a while ago and who has programmed extensively though not in Python. The goal at the end is to have a deep understanding of the LLM space and its adjacencies.

How long has it been since you studied/used university-level math? Calculus and linear algebra in particular. I ask because it’s pretty difficult to get through the math of backprop without a firm grasp of these. The Python part is trivial by comparison, the main difficulty being the matching of dimensions.

It's nothing more than the chain rule...University level it is not...the engineering aspect is the non trivial part IMHO...
Post reply on HN