API Guy.

He has a serious regex problem.
Regexes are never easy to read, but the ones he uses just take the cake. They're either blatantly wrong, or totally over-engineered garbage that somehow still lacks basic functionality. I think "garbage" here is a little too nice, since you can tell what garbage actually is/was without studying it for five minutes.

In lieu of an actual rant (mostly because I'm overworked), I'll just leave a few samples here. I recommend readying some bleach before you continue reading.

Not a valid url name regex:
VALID_URL_NAME_REGEX = /\A[\w\-]+\Z/

Semi-decent email regex: (by far the best of the four)
VALID_EMAIL_REGEX = /\A[\w+\-.]+@[a-z\d\-.]+\.[a-z]+\z/i

Over-engineered mess that only works for (most) US numbers:
VALID_PHONE_REGEX = /1?\s*\W?\s*([2-9][0-8][0-9])\s*\W?\s*([2-9][0-9]{2})\s*\W?\s*([0-9]{4})(\se?x?t?(\d*))?/

and for the grand finale:

ZIP_CODE_REGEX = /(^\d{5}(-\d{4})?$)|(^[ABCEGHJKLMNPRSTVXY]{1}\d{1}[A-Z]{1} *\d{1}[A-Z]{1}\d{1}$)|GIR[ ]?0AA|((AB|AL|B|BA|BB|BD|BH|BL|BN|BR|BS|BT|CA|CB|CF|CH|CM|CO|CR|CT|CV|CW|DA|DD|DE|DG|DH|DL|DN|DT|DY|E|EC|EH|EN|EX|FK|FY|G|GL|GY|GU|HA|HD|HG|HP|HR|HS|HU|HX|IG|IM|IP|IV|JE|KA|KT|KW|KY|L|LA|LD|LE|LL|LN|LS|LU|M|ME|MK|ML|N|NE|NG|NN|NP|NR|NW|OL|OX|PA|PE|PH|PL|PO|PR|RG|RH|RM|S|SA|SE|SG|SK|SL|SM|SN|SO|SP|SR|SS|ST|SW|SY|TA|TD|TF|TN|TQ|TR|TS|TW|UB|W|WA|WC|WD|WF|WN|WR|WS|WV|YO|ZE)(\d[\dA-Z]?[ ]?\d[ABD-HJLN-UW-Z]{2}))|BFPO[ ]?\d{1,4}/

^ which, by the way, doesn't match e.g. Australian zip codes. That cost us quite a few sales. And yes, that is 512 characters long.

Relatable?

I just saw a LinkedIn post where some guy suggested to fix gender bias in Google Translate by a few lines of JS code replacing every "he" or "she" by "he/she". Damn, give that man a Turing award, he solved the problem of biased models!!!
I love that his solution is completely contextless and will allow men to finally have periods and give births. Also, that the idea of bias embedded in models in general is completely lost on him.

Manager: How to make successful product?
CEO: Just Add words like Machine learning and Ai
Newbie developers: Takes 10$ udemy course without statistical and probabilistic knowledge, after 1 week believes himself to have "Expertise in ML,AI and DL"
HR: Hires the newbie
*Senior Developer Quits*

The hype of "we will have autonomous cars by 2025" reminds me the 2017 hype of "we will have autonomous cars by 2021". I guess if you try this PR trick enough times, eventually you will get it right.

Can someone explain me AI/ML/DL in traditional algorithmic way without AI jargons?

What I currently understand is that they convert the training data to numbers based on a complex black boxed mathematical algorithm and then when a new data comes in, the same conversion is done and a decision is taken based on where the the new number fits in within the geometry/graph plot of the old numbers from training. The numbers are then updated. Is this what they call AI? Nearest number/decision search?

Kindly try to avoid critic, I am having a difficult time understanding the already trending AI stuff. People say that the algo exists from long back but only now we have the compute power.

Well..

A guy that I know from uni (used to be my TA), asked me to help him with data collection for his thesis.
The thing is, there are plenty of other people that he is better acquainted with (and are my friends), and he did not ask them.
So I was wondering, DS/DL/ML people, is this how you make a move on a girl? Ask her to collect data with you?

I wonder what would happen if someone trains a chat-bot based on Posts and Comments on Devrant? a Psychopathic Chatbot?

Deep learning is probably (????) the only research branch where every successful paper title needs to be a stupid acronym or meme

I work in a conversational AI startup and the new intern that joined yesterday didn't understand half the memes or acronyms (especially all the Simpsons related) because apparently he's "Gen Z" and all the paper title is "Millennial" humour

He's only 2 years younger than me. Am I literally at the millennial - GenZ border ? Or the intern is out of touch ?

First Kaggle kernel, first Kaggle competition, completing 'Introduction to Machine Learning' from Udacity, ML & DL learning paths from Kaggle, almost done with my 100DaysOfMLCode challenge & now Move 37 course by School of AI from Siraj Raval. Interesting month!

Working full time as a "Protocol Engineer" for a big company, taking care of pretty much everything related to AS/NAS on the network layer (2G, 3G, 4G).

I hate it, but it pays really well.

On my free time, revising ML/DL stuff from Udacity's nano (finished it last year) while studying for the VR nano and keeping my coding skills fresh (basic to advanced structures, coding strategies, best practices and stuff).

Love it, but usually I pay a heavy price to keep my mind in place.

Sometimes I just wish to give everythin up and travel the world with my 2 bucks and just try to get some rest. :v

To all of you who go through this kind of stuff, how are you holding up?

People started to use ChatGPT to discover a new vulnerabilities (0day), I saw someone use it to help them break a smart contract, I mean if you already found a 0day you might ask it to write the exploit rather write it yourself 😬

My life is like a random number, trying to find the best distribution to follow.

-A frustrated ML Student..haha

Is there a place to find standard models open-sourced by Academia and Companies ?

Any GitHub repository?

Has anyone (who does either Data Analysis, ML/DL, NLP) had issues using AMD GPUs?
I'm wondering if it's even worth considering or if it's too early to think about investing in computers with such GPUs.

I want to learn ML and DL at least at a good scale in 3 months. What should i do and how do i learn enough to create a basic projects ?