joaogui1

I really don't understand why they didn't go with Julia, it fits the purpose much better than Swift, already having a ML ecosystem and having great interop with Python, C, C++, R and Matlab. Heck JAX, where a lot of TF refugees are going, is pretty similar to Zygote

You don't need the matrix type in Numpy, it's just syntax sugar over a 2-dimensional array

How hard is it to live in Taiwan speaking only English? Is their official language Mandarin or is it some different Chinese dialect? How safe are Taiwan streets?

* How do I create a program (in this language)?

* How do I store a program?

* How do I run a program (in this language)?

* How do I get the program to output something (in this language)? You don't run C programs by calling python a.c, you run them by first compiling and then running the result. And yeah, I put the link there to illustrate what I was talking about, and it's not about why and how you learn a new programming language.

Settings -> Hotkeys

I've migrated from Roam to Obsidian and it's really awesome. I have ownership of my files, there are constant updates with newer features and bug fixes and the community is really nice :)

It's (Google) Brain float

For the IDE part they have written some cool debuggers that help you understand the differentiation part and catch bugs on it. But I'm not sure why you couldn't just use the debugger, instead of a whole new IDE, much less why you would need a new RCS

Any idea about flax? And how do flax and Trax interact?

What kind of feedback? They never gave me any specific feedback when I interviewed

If vertical farming becomes widespread we could free a lot of land from farms and use them to produce palm oil. Besides that plant based food is more efficient than meat based, as meat based you need to feed the animal and then eat the animal

Machine Learning:

* https://www.youtube.com/watch?v=UzxYlbK2c7E: Andrew Ng's machine Learning course, the recommended entry point by most people

* https://mlcourse.ai/ : More kaggle focused, but also more modern and has interesting projects

Do both courses simultaneously, take good notes, write useful flashcards, and above all do all the exercises and projects

Deep Learning

* https://www.fast.ai/ - Very hands-on, begin with " Practical Deep Learning for Coders" and then "Advanced Deep Learning for coders"

* https://www.coursera.org/specializations/deep-learning : More bottom-up approach, helps to understand the theory better

Do those two courses in parallel (you can try 2 weeks of coursera followed by one of fastai in the beginning, and then just alternate between them), take notes, write good flashcards and above all do the exercises and projects.

After that you will be done with the beginning, your next step will depend on what area interested you the most, and getting way too many resources right now can be extremely confusing, so I would recommend doing a follow-up post after you worked through the above resources. Also as non-ML stuff I recommend Scott Young's Ultralearning and Azeria's self improvement posts (https://azeria-labs.com/the-importance-of-deep-work-the-30-h...)

Since he compared to gaming I think he's saying it's low