ML for Beginners GitHub

Relatedly, I recommend Andrej Karpathy’s “neural net: zero to hero” YouTube lectures. He breaks things down to the fundamentals in this really nice way, and you follow along in your own jupyter notebook.

Is there a polite, constructive way to tell him no word has been put into the "original author's mouth"?

What he's reading there is just the following sentence in the book, separated with a comma...

> TextBlob does a pretty good job at the translation: "C'est une vérité universellement reconnue, qu'un homme célibataire en possession d'une bonne fortune doit avoir besoin d'une femme!".

It can be argued that TextBlob's translation is far more exact, in fact, than the 1932 French translation of the book by V. Leconte and Ch. Pressoir:

"C'est une vérité universelle qu'un célibataire pourvu d'une belle fortune doit avoir envie de se marier, et, si peu que l'on sache de son sentiment à cet egard, lorsqu'il arrive dans une nouvelle résidence, cette idée est si bien fixée dans l'esprit de ses voisins qu'ils le considèrent sur-le-champ comme la propriété légitime de l'une ou l'autre de leurs filles."

In this case, the translation informed by ML does a better job than the human translator who is unnecessarily putting words in the original author's mouth for 'clarity'.

https://github.com/microsoft/ML-For-Beginners/blob/main/6-NL...

i dont really understand what an LLM is going to do for you in probably 99% of all problems that have an ML solution except maybe write a block of code using sklearn or whatever to get you started. There are several reasons for this:

1. there is no reason to pay the api costs for an LLM to ingest data for you and do something with it when basically all it will be doing is writing the python codes for you that you will eventually be using

2. the LLM doesn't represent some sort of conceptual understanding of whatever you are trying to do to solve your ML problem, so you can't rely on it to be clever and answer questions or brain storm new ideas

3. even if you have a reason to use an LLM in some data processing pipeline it will only be one stop on the information super highway you are trying to create. you probably are going to use it to do something, but you probably also are going to be doing other things (e.g., image segmentation, time series analysis, etc.).

LLMs are great. but they are really just like, one more tool to have, they aren't the only tool.

Looks pretty nice (high-level skimming of content .. I work in the field but more DL focused. Mathy ML is my nemesis).

Two questions related to learning in general:

1) I feel I have a good undergrad level grasp of ML but not at a grad level grasp. The math is a bit overwhelming. I am not a fan of conferences like Neurips. Any one else try to conquer this challenge and have a story to share?

2) A bit off topic but the XR course they have linked on the page is also cute. It lists both Unity and MRTK. I have a Hololens2 and am curious about spatial mapping and awareness (just to learn at the moment). Any suggestions on what is a good stack for this area? I have very little 3D graphics background .. unity seems a bit too high for serious work and an engine seems too level :(

How about using an LLM to help you write a MuZero-like model designed for a specific task? (Also MuZero took like 12 hours to train on old hardware, so my MacBook might be good enough here) obviously we’re not here yet, but it doesn’t seem far away. Hell, you could train a small LLM specifically just to do this.