HAO

Data Scientist | Kaggle Competition Master

[HAO @lihaorocky]

@JFPuget Guess it’s because in China there is lunch nap which is normally 30~60 minutes both at school and most workplaces

[HAO @lihaorocky]

@senkin13 Yes, in kaggle tabular competitions thousands teams used the same GBDT algorithm, but only few can achieve great scores, similar with transformer to LLMs.

[HAO @lihaorocky]

@JFPuget Yeah, we can also see this in kaggle.

[HAO @lihaorocky]

@shengxj1 我跟你情况差不多，不过是去年5月份从北京来伦敦工作的，老婆一块儿来的，她直接尝试找工作3个月不太顺利现在申请到了伦敦这边的大学马上去上学。我们的方式是来伦敦后开始学打网球，每周打一两次，夫妻之间有个一块儿从事的体育爱好也挺好的。

[HAO @lihaorocky]

LLMs are too stupid to solve brain teasers and prob challenges.

[HAO @lihaorocky]

@hydantess1993 Yes, but ChatGPT will make the document work much easier. 😁

[Andrej Karpathy @karpathy]

# on technical accessibility

One interesting observation I think back to often:
- when I first published the micrograd repo, it got some traction on GitHub but then somewhat stagnated and it didn't seem that people cared much.
- then I made the video building it from scratch, and the repo immediately went through hockey stick growth and became a verty often cited reference for people learning backpropagation.

This was interesting because the micrograd code itself didn't change at all and it was up on GitHub for many months before, stagnating. The code made sense to me (because I wrote it), it was only ~200 lines of code, it was extensively commented in the .py files and in the Readme, so I thought surely it was clear and/or self-explanatory. I was very happy with myself about how minimal the code was for explaining backprop - it strips away a ton of complexity and just gets to the very heart of an autograd engine on one page of code. But others didn't seem to think so, so I just kind of brushed it off and moved on.

Except it turned out that what stood in its way was "just" a matter of accessibility. When I made the video that built it and walked through it, it suddenly almost 100X'd the overall interest and engagement with that exact same piece of code. Not only from beginners in the field who needed the full intro and explanation, but even from more technical/expert friends, who I think could have understood it if they looked at it long enough, but were deterred by a barrier to entry.

I think as technical people we have a strong bias to put up code or papers or the final thing and feel like things are mostly self-explanatory. It's there, and also it's commented, there is a Readme, so all is well, and if people don't engage then it's just because the thing is not good enough. But the reality is that there is still a large barrier to engage with your thing (even for other experts who might not feel like spending time/effort!), and you might be leaving somewhere 10-100X of the potential of that exact same piece of work on the table just because you haven't made it sufficiently accessible.

TLDR: Step 1 build the thing. Step 2 build the ramp. 📈
Some voice in your head will tell you that this is not necessary, but it is wrong.

[HAO @lihaorocky]

If you don't know why one thing is so good, then there must be something wrong happening about it. Keep low-key until it's fully verified. - a lesson

[Zhengzhong Tu @_vztu]

Reinforcement learning explained

https://t.co/hs4pE5fS2r

[HAO @lihaorocky]

Found it's weird that in pandas, groupby-diff is way more slow than groupby-shift operation...

[Matei Zaharia @matei_zaharia]

Our new MOOC on #LLM Foundation Models from the Ground Up is now available! Join me, Chengyin Eng, @samraymond, Joseph Bradley and @abhi_venigalla for a detailed look at how LLMs are built, how to improve them, and where the field is going.
https://t.co/87xAnYTHLE

[Peter Shor @PeterShor1]

I've put my lecture notes from my Fall 2022 Quantum Computation course online.

https://t.co/A4ycjJXuoF

I don't guarantee that there aren't any mistakes left in them, although I've tried to eradicate them all. If you find any, feel free to email me.

[Santiago @svpino]

Another deep learning breakthrough:

Deep TDA, a new algorithm using self-supervised learning, overcomes the limitations of traditional dimensionality reduction algorithms.

t-SNE and UMAP have long been the favorites. Deep TDA might change that forever.

Here are the details:

[Andrej Karpathy @karpathy]

MusicGen 🎶 is awesome and very fun to play with. Thank you Meta for the release. The inference code [1] looks very nice & clean.
[1] https://t.co/2nMyXejnzW

[Piotr Pomorski @PtrPomorski]

Another interesting repo to check out, FinGPT: https://t.co/QAAq2fp0ey; applying AGI to asset allocation and trading. The caveat is, it thrives on OpenAI API, would be better it the LLM was free but worry not, I want to do some research after my PhD is done.

[HAO @lihaorocky]

@ph_singer @kaggle Maybe ChatGM means the post-process part😂

[Reorx @novoreorx]

今天发现的新工具 ChatPDF 简直是学习神器，上传 PDF 文件后，可以对它提问任何关于这份 PDF 的问题，非常适合快速提取各种 paper 论文的摘要，也支持中文输出。几天前我还在一段段地从 PDF 里复制到 ChatGPT，没想到生活这么快就被改变了🤯
https://t.co/ereNCVsR1a

[Vadim Irtlach @vadimirtlach]

Exciting news! I just published an article on Kaggle describing some of the most popular and powerful Semi-Supervised Learning approaches, including Pseudo Labeling, Meta Pseudo Labels, and Noisy Student.

https://t.co/Olo5j3Tyo7

[HAO @lihaorocky]

@zakopur0 It happened to me in H&M.

[HAO @lihaorocky]

From tomorrow finally I can devote most of my time in competitions. So excited.