#python

This post is a retro on how I learned Python.

Disclaimer: I can not claim that I am a master of Python. This post is a retrospective of how I learned Python in different stages.

I started using Python back in 2012. Before this, I was mostly a Matlab/C user.

Python is easy to get started, yet it is hard to master. People coming from other languages can easily make it work but will write some "disgusting" python code. And this is because Python people talk about "pythonic" all the time. Instead of being an actual style guide, it is rather a philosophy of styles.

When we get started, we are most likely not interested in [PEP8](https://peps.python.org/pep-0008/) and [PEP257](https://peps.python.org/pep-0257/). Instead, we focus on making things work. After some lectures from the university (or whatever sources), we started to get some sense of styles. Following these lectures, people will probably write code and use Python in some projects. Then we began to realize that Python is strange, sometimes even doesn't make sense. Then we started leaning about the philosophy behind it. At some point, we will get some peer reviews and probably fight against each other on some philosophies we accumulated throughout the years.

The attached drawing (in comments) somehow captures this path that I went through. It is not a monotonic path of any sort. This path is most likely to be permutation invariant and cyclic. But the bottom line is that mastering Python requires a lot of struggle, fights, and relearning. And one of the most effective methods is peer review, just as in any other learning task in our life.

Peer review makes us think, and it is very important to find some good reviewers. Don't just stay in a silo and admire our own code. To me, the whole journey helped me building one of the most important philosophies of my life: embrace open source and collaborate.

#visualization


https://anvaka.github.io/map-of-reddit/?x=175273.66777410256&y=370576.01346498774&z=217281.8913341138

非常详细和深入
https://zhenzhongxu.com/the-four-innovation-phases-of-netflixs-trillions-scale-real-time-data-infrastructure-2370938d7f01

#ml

Finally... We can now utilize the real power of M1 chips.


Introducing Accelerated PyTorch Training on Mac | PyTorch
https://pytorch.org/blog/introducing-accelerated-pytorch-training-on-mac/


I have been following this issue: https://github.com/pytorch/pytorch/issues/47702#issuecomment-1130162835
There were even some fights. 😂

#misc

Quote from this article:

"It doesn’t transmit from person to person as readily, and because it is related to the smallpox virus, there are already treatments and vaccines on hand for curbing its spread. So while scientists are concerned, because any new viral behaviour is worrying — they are not panicked."

https://www.nature.com/articles/d41586-022-01421-8

#github

I have been following an issue on math support for github markdown (github/markup/issues/274).

One thousand years later ...

Math support in Markdown | The GitHub Blog
https://github.blog/2022-05-19-math-support-in-markdown/

#ml

Parsimony with cognitive resource limitations 🤔

https://www.nature.com/articles/s41586-022-04743-9

#ml

I have heard about deepeta before but never thought it was a transformer.

According to this blog post by uber, they are using an encoder decoder architecture with linear attention.

This blog post also explains how they made a transformer fast.

DeepETA: How Uber Predicts Arrival Times Using Deep Learning
https://eng.uber.com/deepeta-how-uber-predicts-arrival-times/

#ml

This is hilarious.

Source:
https://mobile.twitter.com/arankomatsuzaki/status/1529278580189908993

Paper: https://arxiv.org/abs/2205.11916

#fun

Higharc is a start-up helping people design houses using generative designs.

The demo looks amazing.

https://higharc.com/

#data

If you are building a simple dashboard using python, streamlit is a great tool to get started. One of the problems in the past was to create multipage apps.

To solve this problem, I created a template for multipage apps a year ago.
https://github.com/emptymalei/streamlit-multipage-template

But today, streamlit officially introduced multipage support. And it looks great. I haven’t built any dashboards for a while, but to me, this is still the go-to solution for a dashboard.
https://blog.streamlit.io/introducing-multipage-apps/

#ml

This is also like one thousand years later...

PyMC 4.0 Release Announcement — PyMC project website
https://www.pymc.io/blog/v4_announcement.html

#ml


Mitchell M, Wu S, Zaldivar A, Barnes P, Vasserman L, Hutchinson B, et al. Model cards for model reporting. Proceedings of the Conference on Fairness, Accountability, and Transparency. New York, NY, USA: ACM; 2019. doi:10.1145/3287560.3287596

https://arxiv.org/abs/1810.03993

#fun

😂😂😂

[P] No, we don't have to choose batch sizes as powers of 2: MachineLearning
https://www.reddit.com/r/MachineLearning/comments/vs1wox/p_no_we_dont_have_to_choose_batch_sizes_as_powers/

#fun

The History of User Interfaces
https://history.user-interface.io/

#ml

1.0!

https://modelserving.com/blog/introducing-bentoml-10

#ml

The recommended readings serve as a good curriculum for transformers.

https://web.stanford.edu/class/cs25/index.html#course