This is decent for what it is. Some of the problems are pretty open ended which has pros and cons, but that is very different from leetcode, which has very specific data and test cases.
For example, implement linear regression but the example solution uses a random number generator without a fixed seed. It’s fine, reproducibility isn’t the point, but leetcode problems are more structured.
In leetcode they usually don’t tell you exactly what data structure you must use, only that it must pass certain test cases. By analogy this might not tell you which architecture to use but require that it passes certain eval metrics.
Most ML problems in real life don’t constrain you to use linear regression or a CNN either. But there will be some metric you need to optimize.
What would take this repo to the next level is to have a reproducible data generation function for each exercise as well as a reasonable metric which must be passed. I don’t see anything that requires my classification auc to be over 0.5 which would be a basic criteria of bug-free code.
One time my teacher used a computer to make a math test for me, but then told me I couldn't use my computer during the exam. I dropped out of school immediately.
The purpose of the project is learning. The author believes that avoiding GPT will help you learn more effectively and offers that as upfront guidance. In this case, “avoid using GPT” isn’t an ethical directive but simply a learning recommendation. The value of that advice isn’t tied to which tools were used to create the question set.
This is decent for what it is. Some of the problems are pretty open ended which has pros and cons, but that is very different from leetcode, which has very specific data and test cases.
For example, implement linear regression but the example solution uses a random number generator without a fixed seed. It’s fine, reproducibility isn’t the point, but leetcode problems are more structured.
In leetcode they usually don’t tell you exactly what data structure you must use, only that it must pass certain test cases. By analogy this might not tell you which architecture to use but require that it passes certain eval metrics.
I hoped that it would be a little open ended as most questions in ML in real life are open ended.
Most ML problems in real life don’t constrain you to use linear regression or a CNN either. But there will be some metric you need to optimize.
What would take this repo to the next level is to have a reproducible data generation function for each exercise as well as a reasonable metric which must be passed. I don’t see anything that requires my classification auc to be over 0.5 which would be a basic criteria of bug-free code.
It's also what most people ask when they go for interviews.
I was reverse engineering the ML interview pipeline for myself and that's how I stumbled upon all this.
I think the data aspect does make sense tho. I might add that as the next thing to do
Cool idea, will try. Since it seems mostly llm generated you could publish the process and prompts for transparency.
I'll do that. I'll also add a disclosure that I did use Gpt to generate it.
> Avoid using GPT. Try to solve these problems on your own. The goal is to learn and understand PyTorch concepts deeply.
I mean...this entire project appears to be mostly GPT-generated?
One time my teacher used a computer to make a math test for me, but then told me I couldn't use my computer during the exam. I dropped out of school immediately.
Great analogy my brother there’s minimal difference between a word processing software and an LLM
The purpose of the project is learning. The author believes that avoiding GPT will help you learn more effectively and offers that as upfront guidance. In this case, “avoid using GPT” isn’t an ethical directive but simply a learning recommendation. The value of that advice isn’t tied to which tools were used to create the question set.
Did GPT write this?
Why do you think it I GPT generated?
What are people's other "go try to build this thing, perfectly aligned to your noob-level" ways of learning lower-level ML Tools (PyTorch, CUDA etc.)?
It helps me a lot!!
Is it just me or does anyone else find the red squiggly lines under Pytorch and Leet hilarious in the heading picture?
Ah god damn it.
super helpful, thanks for sharing!