#DS

Just in case you are also struggling with Python packages on Apple M1 Macs


I am using the third option: anaconda + miniforge.

https://www.anaconda.com/blog/apple-silicon-transition

#visualization

Hmmm my plate is way off the planetary heath diet recommendation.

Source:
https://www.nature.com/articles/d41586-021-03612-1

#ml #rl

How to Train your Decision-Making AIs
https://thegradient.pub/how-to-train-your-decision-making-ais/

The author reviewed  "five types of human guidance to train AIs: evaluation, preference, goals, attention, and demonstrations without action labels".

The last one reminds me of the movie Finch. In the movie, Finch was teaching the robot to walk by demonstrating walking but without "labels".

#DS #visualization

https://percival.ink/

A new lightweight language for data analysis and visualization. It looks promising.

I hate jupyter notebooks and I don't use them on most of my projects. One of the reasons is low reproducibility due to its non-reative nature. You changed some old cells and forgot to run a cell below, you may read wrong results.
This new language is reactive. If old cells are changed, related results are also updated.

#ML #Transformers


Alammar J. The Illustrated Transformer. [cited 14 Dec 2021]. Available: http://jalammar.github.io/illustrated-transformer/

So good.

#visualization #fun

https://www.githubwrapped.com/

#ml #science

I remember several years ago when I was still doing my PhD, there's this contest about predicting protein structure and none of them was working well. At that time, I would never have thought we could have anything like AlphaFold in a few years.
.

https://www.science.org/content/article/breakthrough-2021

#visualization

Pu X, Kay M. A probabilistic grammar of graphics. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. New York, NY, USA: ACM; 2020. doi:10.1145/3313831.3376466
Available at: https://dl.acm.org/doi/10.1145/3313831.3376466

A very good read if you are visualizing probability densities a lot.
The paper began with a common mistake people make when visualizing densities. Then they proposed a systematic grammar of graphics for probabilities. They also provide a package (quite preliminary, see here https://github.com/MUCollective/pgog ).

#data #ds

Disclaimer: I'm no expert in state diagram nor statecharts.

It might be something trivial but I find this useful: Combined with some techniques in statecharts (something frontend people like a lot), state diagram is a great way to document what our data is going through in data (pre)processing.

For complicated data transformations, we can make the corresponding state diagram and follow your code to make sure it is working as expected. The only thing is that we are focusing on the state of data not any other system.

We can use some techniques from statecharts, such as hierarchies and parallels.

State diagram is better than flowchart in this scenario because we are more interested in the different states of the data. State diagrams automatically highlights the states and we can easily spot the relevant part in the diagram and we don’t have to start from the beginning.

I documented some data transformations using state diagrams already. I haven't tired but it might also help us document our ML models.


References:
1. https://statecharts.dev
2. https://en.wikipedia.org/wiki/State_diagram

#python

I thought it was a trivial talk in the beginning.
But I quickly realized that I may know every each piece of the code mentioned in the video but the philosophy is what makes it exciting.

He talked about some fundamental ideas of Python, e.g., protocols.

After watching this video, an idea came to me. Pytorch lightning has implanted a lot of hooks in a very pythonic way. This is what makes pytorch lightning easy to use. (So if you do a lot of machine learning experiments, pytorch lightning is worth a try.)

https://youtu.be/cKPlPJyQrt4

#visualization

Beautiful, elegant, and informative. It reminds me of the Netflix movie chromatic storytelling visualization.

Full image:
https://zenodo.org/record/5828349

Other discussions:
https://www.reddit.com/r/dataisbeautiful/comments/s6vh8k/dutch_astronomer_cees_bassa_took_a_photo_of_the/

#ds

Deepnote supports Great Expectations (GE) now.

I ran their template notebook:

https://deepnote.com/project/Reduce-Pipeline-Debt-With-Great-Expectations-mLT9DFCQSpW4kUBAzzdhBw/%2Fnotebook.ipynb/#00000-e170fae0-7e06-4a7a-85f3-343584ec4b94

#visualization

Seaborn is getting a new interface.

Would be great if the author defines a dunder method _ _ add _ _ () instead of using .add() method. Using dunder add, we can simply use + on layers.

Nevertheless, we can all move away from plotnine when the migration is done.

https://seaborn.pydata.org/nextgen/

确实有很多，比如我用 ack 替代了 grep，速度快了不少。

https://www.ruanyifeng.com/blog/2022/01/cli-alternative-tools.html

#ml

Lol, DeepMind and OpenAI:

https://deepmind.com/blog/article/Competitive-programming-with-AlphaCode

vs

https://openai.com/blog/formal-math/

#ML

I made some slides to bootstrap a community in my company to share papers on graph related methods (spectral, graph neural networks, etc).
These slides are mostly based on the first two chapters of the book by William Hamilton. I added some intuitive interpretations on some key ideas. Some of these are frequently used in graph neural networks even transformers. Building intuitions helps us unboxing these neural networks. But the slides are only skeleton notes so I probably have to expand them at some point.

I am thinking about drawing more about the book and on this topic. Maybe even making some short videos using these slides. Let's see how far I can go. I am way too busy now. (<-no excuse)

#ML #RL #DeepMind



Magnetic control of tokamak plasmas through deep reinforcement learning | Nature
https://www.nature.com/articles/s41586-021-04301-9