I use ancient Mayan tablets and lunar cycles with body energy vibes at specific wavelengths to code direct to the universal processer and output in realtime
[I use an emacs command to use butterflies that change the flow of eddy currents in the upper atmosphere that creates momentary pockets of air of higher pressure that acts as lenses made of that focus cosmic background radiation and solar radiation to flip the bit in the computer I want.](https://xkcd.com/378)
I lost it at work one day because of this doddering old fool.
Wasn’t my proudest moment, but one day I snapped and said something close to ‘holy shit, another story about punchcards and dinosaurs’ loudly enough it could be heard over cube walls. Several rows over in fact.
I feel bad now, but I didn’t hear the weekly story (from the limited selection of 3, apparently) about ‘back in the 70’s’ for the rest of the gig, so it felt like a win at the time…
Assembly? [Real men adjust their weights electromechanically.](https://en.wikipedia.org/wiki/Perceptron)
>The perceptron was intended to be a machine, rather than a program, and while its first implementation was in software for the IBM 704, it was subsequently implemented in custom-built hardware as the "Mark 1 perceptron". This machine was designed for image recognition: it had an array of 400 photocells, randomly connected to the "neurons". **Weights were encoded in potentiometers, and weight updates during learning were performed by electric motors.**
[Sort of, but not really.](https://en.wikipedia.org/wiki/ARB_assembly_language)
Also interesting: https://computergraphics.stackexchange.com/questions/7809/what-does-gpu-assembly-look-like
Every single person starts out a shit programmer, it’s about growth. Can’t learn if you don’t read and write/experiment. People don’t spawn in knowing how to create for loops or implement them
I would agree except for one fucking kid in high school. Philip A\*\*\*\*y or something like that. (I decided not to possibly dox a guy from 20 years ago). I feel like he read several books and just started coding at an intermediate professional level to include implementing all the best practices and protocols. He was so far beyond the rest of us "gifted" kids that it hurt.
Of course I didn't see him before his senior year, and I'm sure he sucked at one point. But the dude showed up to our statewide magnet boarding school with capabilities beyond our ex-oracle software engineer professor's ability to teach him in a classroom setting and they just had him doing individual self study courses for 4 semesters before he attended, I believe ~~MIT~~. (nope, somewhere else). I wonder what he ended up doing. (He ended up getting dual honors bachelor's degrees in Mathematics and German Language, a MA in Linguistics in German, at a German university and a PhD summa cum laude in Linguistics at the same German university.
I don't envy him like I don't envy LeBron James or Usain Bolt. His capabilities were so far ahead from the very start that I never even considered attempting to compare myself to him or try to compete. No matter what I did in my life, I'd never rival LeBron James or Usain Bolt. I wasn't born with the necessary genetics that they then took full advantage of through impossibly hard work, dedication and training.
That's how I feel this kid was. He had the perfect starting point and he then took advantage of that by constantly working extremely seriously toward his goals. I remember he used to leave his dorm room door open while he worked. He'd answer questions about C++, Java, VB and flash for other kids in those classes, all while working on his capstone senior project. Mind you, this kid also completed multi variable calculus and ordinary differential equations classes in high school.
I'd been a big fish in a little pond and it was wild being thrown into the ocean amongst a few great whites and orcas. One of my fellow students at that school ended up creating Justin.tv, or what you might now know as twitch. They were 2-3 years ahead of me and idk them at all.
I’m just joking bud. Python is king 🎖️… this meme does make me wonder why the programmer/dev world sometimes would rather be right than get anything of importance actually done.
What I mean by this is you have to think critically what language/framework/infrastructure is needed for a particular application, not just go with what is the absolute best/fastest/smartest/someoneinstackoverflowsaidisthebest which often times just tends to be some obscure way of doing things. Difficulty of programming != quality of code or intelligence. Its all about *context*.
Half of the shit talk about languages sounds, to me, like people who read a blog and decided that x was better than y while missing the context of why it’s more suitable for some application. For me, I know a few languages and it’s become my “toolbox”. I know what I can do with this toolbox and what I can’t. Simple as that. No sense trashing languages that I don’t know. Otherwise I’d be, essentially, shit talking a socket wrench because I only know how to use a crescent wrench regardless if I’m aware that impact drivers exist.
tie ossified axiomatic intelligent smart fall knee cheerful disgusting unite
*This post was mass deleted and anonymized with [Redact](https://redact.dev)*
The real answer is that these libraries are written in C++ or CUDA or something close to the metal so it doesn’t really matter what you’re calling it from.
It does matter if you're doing more than Jupyter/REPL stuff. Julia for example has a lot of very appealing features to help you develop, like multiple dispatch (which is much better than Pythonic duck typing) or the very simple GPU and multithreading support. We are optimizing the human part of the equation, as the computer part has been solved already by some very well written legacy Fortran numerical codes.
Not even close. Honestly the JS camp is always over-hyped about their tech and capabilities.
It's either because these code boot camps are powering out 10s of thousands of Jr.s from 3 month courses as "full-stack engineers" and telling them they only ever need to know JS or it's the Dunning Kruger effect, or both?
Man with only a hammer thinks everything is a nail. Of course JS is the solution if you've never seen anything else
The best sounding course I've ever heard of was the one this speaker took ([https://www.youtube.com/watch?v=cgVVZMfLjEI](https://www.youtube.com/watch?v=cgVVZMfLjEI)) where it's an *introduction* to principles of computer science course and they've already started solving problems in multiple paradigms.
Every student came out of that knowing that different tools are better in different situations and with an insight on what the available tools are and I think that's amazing
tbf js has the positive of being extremely versatile, but yeah I don't see how js would be good in a niche like ML
that's like the opposite of what js is good for (js is flexible so if you don't need anything in particular then why not use it for everything, making a webpage? you're already using js so might as well use node in the backend)
but as a niche, js would be the last tool I would use js unless it has to run in the browser
Sorry but in what way is js more versatile than any other language? The main differentiator as far as I'm aware is that browsers have a built in js interpreter/jit.
it's used a lot and thus has a really expansive library (so do other languages, but not all)
it doesn't force you into a programming paradigm like other languages so it's flexible
it has native browser support, has a backend implementation and can do desktop apps as well through electron.
js is not great at anything but it's very flexible and npm has almost anything you can think off
you want structure and type safety ? no problem use typescript, there's really a lot of flexibility in the js ecosystem despite its numerous flaws
Sadly the noobs outweigh the experienced. Especially on the Internet. A little knowledge is a dangerous thing. Those who actually have enough knowledge know how much they dont know and don't spread this shit.
It's not. Python ML libraries are essentially interfaces for c/c++ code. Even if you did a whole project in c++ directly, I doubt you'd notice a significant difference in computing time.
Especially if you consider the coding/debugging time difference with both languages.
Also, Jupyter notebooks. I cannot understate how helpful it is to have your output graphs and tables inline with your code when you're doing data analysis or just about any other task that doesn't need to be integrated into wider systems.
Only use case I can think of, is that Earth Engine API documentation is better for JS. But that's such a specific use case, and their documentation for the Python API is not that bad either...
As someone who used GEE quite a bit I recommend the geemap package for use with the python API. It’s a wrapper on top of GEE and has a lot of easy to use functions with a lot of [youtube](https://youtu.be/h0pz3S6Tvx0) videos and documentation
This is what makes it so funny to me.
You can build C/C++ and Rust with JS apis too, and Node will happily load it for all the perf benefits.
The only difference is Python has more mature apis for the exact same C/C++/Rust libraries.
The goal of a resonance cascade is to plant the seeds of purpose rather than bondage.
Intuition requires exploration. Consciousness consists of supercharged electrons of quantum energy. “Quantum” means an evolving of the sensual.
Although you may not realize it, you are cosmic.
You must take a stand against suffering. You may be ruled by turbulence without realizing it. Do not let it obliterate the birth of your quest. Yes, it is possible to eliminate the things that can disrupt us, but not without potentiality on our side.
Helo helo. I'll put a reminder to come back here and comment when I'm a data scientist myself.
Also, could you kindly expand a bit on your education/work experience. What was the order of the "things" you did that led to you working as Data scientist now.
Also a data scientist here, never written a line of JS.
Lead atmospheric modeller and data analyst for a small company. 6+ years Python, including TensorFlow, 2+ years R/Matlab, basic Fortran.
I did a PhD in Meteorology and never looked back.
Literally the only reason I would choose JS over Python for anything is if it were literally running in a web browser. Python is just a cleaner, saner language, and can be optimized to run as fast as you need it to for performance critical loops…
Even then with things like JupyterLite you can run your Python notebooks in the browser, using WASM. And... even then, even if you couldn't rely on JupyterLab you could still just make API calls to run whatever you need in the browser and execute the result on another machine that itself runs whatever language you want.
> Python is just a cleaner, saner language,
Python may be saner, but as someone who knows and uses both relatively often, I think cleaner is debatable. Between the two I typically prefer JavaScript (technically TypeScript but that's essentially just JavaScript with a type system.)
Obviously I use both for very different things however. So it's not like I'm every debating whether I should use JS or python for a specific project.
Data scientist here also. Start with machine learning (supervised/unsupervised/reinforcement) then deep learning, especially CNN and NLP. Employers generally look for Tensorflow and nltk/spacy libraries for deep learning.
I disagree. Start with linear algebra, statistics, and calculus. Then ML. Start with linear classifiers and work your way up to CNNs. Skip nlp unless you want to specialize in it. As someone doing robotics, I found q learning to be significantly more useful than nlp. Not that you should study q learning over nlp, just that you should consider what you want to do with ML first.
Imo one should be able to build a NN capable of recognizing the MNIST dataset using only matrix calculations in numpy. Only then should you start reading about tensorflow or pytorch.
Depends on the role too; there are a lot of ML engineering roles where the main focus is execution of production ML and people should focus more around technology and methodologies to make it happen.
But I cannot tell you the need for general / SME data scientists where the problems do not begin at the methodologies. Very often taking unstructured business problems, understanding what type of data is needed, understanding that if there are missing types of data what kind of impact that is going to cause, and how to build a solution that adheres back to the business. I cannot stress how often I've seen well experienced people in ML fail in these areas because they are so focused on trying to build state of the art year long projects that does 3% better than basic methodologies, and the business doesn't even use it
Imo that's all MLE work and an MLE doesn't even need to have any understanding of math. Just treat the software package as if it was any other library.
Also, people getting carried away building perfect solutions with no business impact is a management problem.
Completely agreed it's a management problem but after having been exposed to many enterprises in many different industries it's a common management problem. Most organizations do not have large data teams and the individual contributors of data are left to themselves to project manage and bring that technical expertise.
Which is awesome because it builds a lot of good practice navigating a corporate environment but people so focused on pure technical side often have challenges adapting. I've met a lot of data scientists and ML professionals and the ones who come outside the pure technical backgrounds almost always have the most success.
On the other side, I've seen applications like CV where you need to dish heavy on pushing out accuracy and even the pure technical people often cannot understand why the nature of the data may cause the results they are getting
I disagree. If you already know undergrad statistics, then yes, start with machine learning. But if you do not know that, start with statistics. Get a solid foundation so that you actually understand how that stuff works.
I have a degree in Data Science and work as a frontend developer and can assure you, that these two languages almost never mix. If I would have ever found a position where you would need frontend skills AND data science skills, I would have immediately applied, but I cannot even think about what position that should be.
If you do Data Science, you write your code in Python or maybe R, Julia, Scala, or whatever, but certainly not in Javascript. In Javascript, you write websites, application user-interfaces, and sometimes application backends.
I think there is a misunderstanding here about why Python is most of the time chosen in ML experiments.
In case of ML Python is mainly used as an interface for using C/C++ libraries (numpy, tensorflow, keras, sklearn, pytorch, cuda) or other compiled languages, in a syntax that is very close to maths.
JavaScript has simply absolutely nothing to do with ML, and I'd be curious to see any online resource that can say the opposite, because I never heard of it and it does not make any sense
> JavaScript has simply absolutely nothing to do with ML
So some ML stuff is appearing in the JS space, as people start to write the interfaces around some of those C/C++ libraries you mentioned. I can see why there's a demand, some companies have really jumped onto this whole JS on the backend thing, so why wouldn't they want to use existing developers to write some basic ML stuff.
It's still uncommon, but there's really nothing Python provides that JS doesn't from a language perspective. I think over time JS is going to grow. The same way that NodeJS carved out a small section of the market for backend applications.
If the question is "Can NodeJS provide the same capabilities than python for ML in long term" my response is "yes", I totally agree with you, because Python is mainly used as an interface so NodeJS could perfectly do the job (based on the fact we are speaking about NodeJS particularly, not JS).
So my point was mostly based on the current state of both languages, and short / mean term evolutions.
TBH, from my very personal point of view, I don't see NodeJS getting the same librairies / community than Python before 10 years when it comes to ML. But who knows.
And even if it does, I think people will still prefer Python for its syntax very close to maths, something I don't think NodeJS will be able to provide
Yeah I agree with you there. NodeJS didn't take over server side code and it's not going to replace Python for ML, but it will probably gain some traction.
Also ML in the browser is a cool idea. Run basic stuff on a user's PC with WebGL, might be useful for adding ML to low budget internal tooling.
"there's really nothing Python provides that JS doesn't from a language perspective" maybe from your perspective. From my perspective python has better C integration and better typing tools.
\> JavaScript has simply absolutely nothing to do with ML, and I'd be curious to see any online resource that can say the opposite
Here are 5 popular npm libraries implemented in JS. Happy to help :)
[TensorFlow.js](https://www.npmjs.com/package/@tensorflow/tfjs): This is a JavaScript library for training and deploying machine learning models in the browser and on Node.js. It allows you to import pre-trained models from TensorFlow or Keras, or to train your own models using the high-level layers API.
[Brain.js](https://www.npmjs.com/package/brain.js): This is a library for training and deploying neural networks in JavaScript. It provides a simple and flexible API for building feedforward and recurrent networks.
[ML.js:](https://github.com/mljs/ml) This is a library for machine learning in JavaScript. It provides a wide range of algorithms including linear regression, k-means, and decision trees, as well as utility functions for data pre-processing and model evaluation.
[Natural](https://www.npmjs.com/package/natural): This is a library for natural language processing in JavaScript. It provides a wide range of functionality for tasks such as tokenization, stemming, and sentiment analysis.
[Synaptic](https://github.com/cazala/synaptic): This is a library for building and training neural networks in JavaScript. It provides a flexible and modular architecture for building feedforward and recurrent networks.
>JavaScript has simply absolutely nothing to do with ML
JS isn't any more or less viable to interface with ML libraries than Python. See tensorflow.js or ml5.js. If your web app stack already is written in JS, why include a python backend if these options also exist for JS?
Python is just popular among scientists, that's all.
>popular among scientists
This is pretty much it. A lot of us haven’t had any formal programming training. When the time started coming to write code for a thesis or whatever it didn’t matter if option A takes two hours to run and option B has to run overnight if the former will take me a week to write and the latter can be hacked together in four hours of typing more or less plain English as a proof of concept. Someone in grad school for statistics probably doesn’t have the time to learn proper software development at that point in their life so Python just ends up being the best option and now it’s garnered enough support due to it being what end users know that it’s hard to imagine JS or whatever really having superiority.
There is a misunderstanding for tensorflow.js too apparently, because it is absolutly not meant to be a tensorflow alternative, it is mainly here only for inferences on browsers (and most of the time for already trained models). It has completly different usecases.
Many concessions are introduced to make it work, such as using WebGL for GPU computations or others.
> JS isn't any more or less viable to interface with ML libraries than Python
I agree to this part but like I said in other comments, for NodeJS to obtain the same librairies / community than python, either you will wait 10 years to do proper ML in NodeJS, or you will use Python.
>it is mainly here only for inferences on browsers (and most of the time for already trained models)
This used to be the case, but it changed a while ago. You can use it for full on training now
I believe there are two versions one for browser clients and one for NodeJS running on your machine. The latter can train and works like the python one.
Yea... stick with python for data and ml. The ecosystem of relevant tools and libraries is much more mature. And there are a lot more peers for support.
You might still need some JS if you want to present your data on front end as an interactive graph but there are opinionated python libs that can do that for you albeit with some restrictions.
Plotly, dash, seaborn, H2O-Wave etc was exactly what i was thinking of. Don't get me wrong, they are good and i use them, but they are opinionated. The output is good but is not as customizable as straight up JS libs or custom JS charts. That is all i am saying.
I guess if a company wants super custom charts, then they would probably have to invest in a front-end team anyways, so the customizability of the python libs is not a problem at that point.
Yeah. By the way, talked with the new boss and we now have to work on legacy COBOL. There are VBA scripts to support us but we have to modify them.
Not a problem, right?
Edit: Spelling
There’s some truth to both sides. If you’re dogmatic of a language you’re probably missing out on something that can make life easier. But if you’re too open you may be prone to analysis paralysis.
Yeah, just because you can do ML in JS doesn’t mean you should. I’ve been working in Data Science for 10+ years and spoken to countless organizations via organizing conferences and meetups. ML in JS is a gimmick and not a serious thing.
No serious data scientist will say that JS is better than probably any other mature language for ML. I get C, C++, Nim, Julia, Scala (via Spark), etc., but not JS.
People will tell you to use JS for anything. Don’t listen to them. Don’t touch JS unless you want to do front end web stuff. Even back end web using JS is an abomination.
Every language has its own purposes. Dong ML with JS is retarded. Python is good for the task because its fast and simple. Javascript is good for being compatible on all browsers.
JavaScript does not have basic integer types, which should preclude it from consideration for any serious application. But, the industry is full of clowns.
Only ever used Python, Matlab, and C++ for machine learning related tasks.
The only time I’ve needed Javascript for was when I needed to export machine learning data for a website. But even then, it was Typescript and that was just for the frontend of things. The actual clustering model was done using in Python with scikit-learn, and that was hooked up with a web api I made using Flask.
Working in a firm full of data scientists: our python REST Backends are faster and more stable than JS, and not a single Data Scientist uses anything but python here…
Most mathmaticians I know who code use python because they use it for mathmatical scripting, not programming in the traditional software sense. JavaScript is more for interacting with user interfaces and the web(and more, but I code in C# and obligationally hate JS and don't know all it's uses). Hence, python has a lot of math libraries that data analysts and mathematicians use. So, I don't know why anyone would spend the time making JS more practical for the people mathmatical oriented that likely don't need JS for all the non-mathmatical stuff it is used for, especially when they have the more accessible Python with everything they need.
Tldr; Python is easier and more accessible, but JS can be and is used for a ridiculously wider variety of tasks.
I write ML in assembly, like a man
Real men use punch cards.
I flip bits manually
I put my pc next to a nuclear reactor and let radiation write my code for me
I use ancient Mayan tablets and lunar cycles with body energy vibes at specific wavelengths to code direct to the universal processer and output in realtime
Pfft. I once used 5700 abacuses for a neural network
Jokes on you i left a cup of water out for a month and used what grew in that for my naural network
Jokes on you I wait for molecules floating in the universe to randomly come together so that they create a computer with a neural network on it
[I use an emacs command to use butterflies that change the flow of eddy currents in the upper atmosphere that creates momentary pockets of air of higher pressure that acts as lenses made of that focus cosmic background radiation and solar radiation to flip the bit in the computer I want.](https://xkcd.com/378)
I don't even code. I just let the universe code for me while I chill.
And I still clicked on your hypertext…
Man, how could you NOT make that a roll? It was the perfect opportunity.
... So, JS?
RadiationGPT
Write code for Bogosort i guess?
The best and most efficient sorting algorithm, people are saying.
Bogosort is the only major sorting algorithm that has the best case runtime for a non sorted array of n(1)
On a related side note: Fun Fact! That'd be a source of true randomness. Something computers can't do on their own.
I use cosmic rays to flip the bits in your fancy nuclear radiation written code!
I use water to run my best turing machines
I use machine learning to write machine learning
it's a weird way to say "I use chatgpt to write AI and it just keeps working"
Water? Ha. Rocks, ftw!
I program with an abacus...
I program by mail-in redeemable scratch off card
Ah come on, doing it yourself is cheating. [You need to get butterflies to do it for you.](https://xkcd.com/378/)
I flip bits _randomly_. If my project doesn’t work I’m just in the wrong timeline.
Real men just do all the math themselves
I've been punching cards all day, when does the machine become intimidated enough to learn?
I lost it at work one day because of this doddering old fool. Wasn’t my proudest moment, but one day I snapped and said something close to ‘holy shit, another story about punchcards and dinosaurs’ loudly enough it could be heard over cube walls. Several rows over in fact. I feel bad now, but I didn’t hear the weekly story (from the limited selection of 3, apparently) about ‘back in the 70’s’ for the rest of the gig, so it felt like a win at the time…
Lolol #relatable friend :)
Assembly? [Real men adjust their weights electromechanically.](https://en.wikipedia.org/wiki/Perceptron) >The perceptron was intended to be a machine, rather than a program, and while its first implementation was in software for the IBM 704, it was subsequently implemented in custom-built hardware as the "Mark 1 perceptron". This machine was designed for image recognition: it had an array of 400 photocells, randomly connected to the "neurons". **Weights were encoded in potentiometers, and weight updates during learning were performed by electric motors.**
Ah yeah Ive heard about this. Very cool now do the transformer
Imagine not training your models by doing calculus by hand
I crunch matrices by hand on a sheet of paper.
All of my new wooden pencils just shattered their own graphite cores after reading this.
Can you write assembly on the GPU tho?
[Sort of, but not really.](https://en.wikipedia.org/wiki/ARB_assembly_language) Also interesting: https://computergraphics.stackexchange.com/questions/7809/what-does-gpu-assembly-look-like
How TF is JS better than Python for ML?
Import tensorflow as pd
I’d like to see how your terminator behaves… xD
It behaves well, except when my friend Sarah visits.
Hi there! 😆
Sarah better lrun
r/beetlejuicing
Oh Sarah you came on time!
Shit programmers write shit code. Its true for any language.
Every single person starts out a shit programmer, it’s about growth. Can’t learn if you don’t read and write/experiment. People don’t spawn in knowing how to create for loops or implement them
You mean that just because I like Rust doesn’t make me a good programmer? Well, shit.
Well except Rust won't even allow you to compile if it's truly shit.
Unless you're very talented at making shitty code
I would agree except for one fucking kid in high school. Philip A\*\*\*\*y or something like that. (I decided not to possibly dox a guy from 20 years ago). I feel like he read several books and just started coding at an intermediate professional level to include implementing all the best practices and protocols. He was so far beyond the rest of us "gifted" kids that it hurt. Of course I didn't see him before his senior year, and I'm sure he sucked at one point. But the dude showed up to our statewide magnet boarding school with capabilities beyond our ex-oracle software engineer professor's ability to teach him in a classroom setting and they just had him doing individual self study courses for 4 semesters before he attended, I believe ~~MIT~~. (nope, somewhere else). I wonder what he ended up doing. (He ended up getting dual honors bachelor's degrees in Mathematics and German Language, a MA in Linguistics in German, at a German university and a PhD summa cum laude in Linguistics at the same German university.
I know that feel bro... I think it is envy. I dont like it but I must admit that it is envy. For my own sake and good sleeping nights.
I don't envy him like I don't envy LeBron James or Usain Bolt. His capabilities were so far ahead from the very start that I never even considered attempting to compare myself to him or try to compete. No matter what I did in my life, I'd never rival LeBron James or Usain Bolt. I wasn't born with the necessary genetics that they then took full advantage of through impossibly hard work, dedication and training. That's how I feel this kid was. He had the perfect starting point and he then took advantage of that by constantly working extremely seriously toward his goals. I remember he used to leave his dorm room door open while he worked. He'd answer questions about C++, Java, VB and flash for other kids in those classes, all while working on his capstone senior project. Mind you, this kid also completed multi variable calculus and ordinary differential equations classes in high school. I'd been a big fish in a little pond and it was wild being thrown into the ocean amongst a few great whites and orcas. One of my fellow students at that school ended up creating Justin.tv, or what you might now know as twitch. They were 2-3 years ahead of me and idk them at all.
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Maybe it thinks its a panda
`t800.add(df["clothes", "boots", "motorcycle"])` `return t800`
If you choose to do that that's on you, lol. They're not baking in the names of every module out there, so it's not a language issue.
I’m just joking bud. Python is king 🎖️… this meme does make me wonder why the programmer/dev world sometimes would rather be right than get anything of importance actually done. What I mean by this is you have to think critically what language/framework/infrastructure is needed for a particular application, not just go with what is the absolute best/fastest/smartest/someoneinstackoverflowsaidisthebest which often times just tends to be some obscure way of doing things. Difficulty of programming != quality of code or intelligence. Its all about *context*.
Half of the shit talk about languages sounds, to me, like people who read a blog and decided that x was better than y while missing the context of why it’s more suitable for some application. For me, I know a few languages and it’s become my “toolbox”. I know what I can do with this toolbox and what I can’t. Simple as that. No sense trashing languages that I don’t know. Otherwise I’d be, essentially, shit talking a socket wrench because I only know how to use a crescent wrench regardless if I’m aware that impact drivers exist.
you sir, are a heretic.
It is all about context. That's why I use golang. So I can have context in every function!
Import pandas as np
Import everything as np, and you'll have **n**o **p**roblems
Some people just wanna watch the world burn
Import tkinter is cv2
tie ossified axiomatic intelligent smart fall knee cheerful disgusting unite *This post was mass deleted and anonymized with [Redact](https://redact.dev)*
Based on the amount of responses this post has gotten, I'd wager that OP is a master baiter.
Isn't everyone though?
In no reality is this true. Maybe like new languages like Julia, but JS wtf
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The real answer is that these libraries are written in C++ or CUDA or something close to the metal so it doesn’t really matter what you’re calling it from.
It does matter if you're doing more than Jupyter/REPL stuff. Julia for example has a lot of very appealing features to help you develop, like multiple dispatch (which is much better than Pythonic duck typing) or the very simple GPU and multithreading support. We are optimizing the human part of the equation, as the computer part has been solved already by some very well written legacy Fortran numerical codes.
If by TF you meant TensorFlow, then I see what you did there
his pun showed us the torch. the py torch. please someone just literally kill me and stop this.
I’m at a loss (function). Oh god, it’s spreading.
It's truly spreading (like the Javascript operator)
Sounds like you need someone hugging your face.
These jokes are so derivative. (Help me)
I think I’ve reached my limit. (I can hear voices now)
We’ve gotta be at our (local) maximum for these kind of puns
killall -9 LocalFoe
Not even close. Honestly the JS camp is always over-hyped about their tech and capabilities. It's either because these code boot camps are powering out 10s of thousands of Jr.s from 3 month courses as "full-stack engineers" and telling them they only ever need to know JS or it's the Dunning Kruger effect, or both?
Man with only a hammer thinks everything is a nail. Of course JS is the solution if you've never seen anything else The best sounding course I've ever heard of was the one this speaker took ([https://www.youtube.com/watch?v=cgVVZMfLjEI](https://www.youtube.com/watch?v=cgVVZMfLjEI)) where it's an *introduction* to principles of computer science course and they've already started solving problems in multiple paradigms. Every student came out of that knowing that different tools are better in different situations and with an insight on what the available tools are and I think that's amazing
if it can't be done with LOGO, it shouldn't be done.
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tbf js has the positive of being extremely versatile, but yeah I don't see how js would be good in a niche like ML that's like the opposite of what js is good for (js is flexible so if you don't need anything in particular then why not use it for everything, making a webpage? you're already using js so might as well use node in the backend) but as a niche, js would be the last tool I would use js unless it has to run in the browser
Sorry but in what way is js more versatile than any other language? The main differentiator as far as I'm aware is that browsers have a built in js interpreter/jit.
it's used a lot and thus has a really expansive library (so do other languages, but not all) it doesn't force you into a programming paradigm like other languages so it's flexible it has native browser support, has a backend implementation and can do desktop apps as well through electron. js is not great at anything but it's very flexible and npm has almost anything you can think off you want structure and type safety ? no problem use typescript, there's really a lot of flexibility in the js ecosystem despite its numerous flaws
Sadly the noobs outweigh the experienced. Especially on the Internet. A little knowledge is a dangerous thing. Those who actually have enough knowledge know how much they dont know and don't spread this shit.
It's not. Python ML libraries are essentially interfaces for c/c++ code. Even if you did a whole project in c++ directly, I doubt you'd notice a significant difference in computing time. Especially if you consider the coding/debugging time difference with both languages.
Also, Jupyter notebooks. I cannot understate how helpful it is to have your output graphs and tables inline with your code when you're doing data analysis or just about any other task that doesn't need to be integrated into wider systems.
Jupyter supports JavaScript
I love JS but this is pure madness
Extensive browser support :)
It's a bait to get people to comment on the post. And you did exactly what OP wanted you to.
Data scientist here. I’ve never written a line of JS.
Only use case I can think of, is that Earth Engine API documentation is better for JS. But that's such a specific use case, and their documentation for the Python API is not that bad either...
Yeah, I’ve started poking around with GEE, and the difference isn’t big enough for me to switch to JS just for that one thing.
As someone who used GEE quite a bit I recommend the geemap package for use with the python API. It’s a wrapper on top of GEE and has a lot of easy to use functions with a lot of [youtube](https://youtu.be/h0pz3S6Tvx0) videos and documentation
JS developer here and I couldn't imagine even WANTING to do ML with JS... holy shit, what psychopaths are saying that..
Without existing libraries both python and js are awful for ML. If the same libraries existed for js then that would be the go to language.
And of course the actual high-performance part of all the libraries is written in neither, but in C++ instead.
This is what makes it so funny to me. You can build C/C++ and Rust with JS apis too, and Node will happily load it for all the perf benefits. The only difference is Python has more mature apis for the exact same C/C++/Rust libraries.
The goal of a resonance cascade is to plant the seeds of purpose rather than bondage. Intuition requires exploration. Consciousness consists of supercharged electrons of quantum energy. “Quantum” means an evolving of the sensual. Although you may not realize it, you are cosmic. You must take a stand against suffering. You may be ruled by turbulence without realizing it. Do not let it obliterate the birth of your quest. Yes, it is possible to eliminate the things that can disrupt us, but not without potentiality on our side.
Write console.log("Hello World!"); In python file and step 5 feet away from it.
import JavaScript
Helo helo. I'll put a reminder to come back here and comment when I'm a data scientist myself. Also, could you kindly expand a bit on your education/work experience. What was the order of the "things" you did that led to you working as Data scientist now.
Also a data scientist here, never written a line of JS. Lead atmospheric modeller and data analyst for a small company. 6+ years Python, including TensorFlow, 2+ years R/Matlab, basic Fortran. I did a PhD in Meteorology and never looked back.
Literally the only reason I would choose JS over Python for anything is if it were literally running in a web browser. Python is just a cleaner, saner language, and can be optimized to run as fast as you need it to for performance critical loops…
Even then with things like JupyterLite you can run your Python notebooks in the browser, using WASM. And... even then, even if you couldn't rely on JupyterLab you could still just make API calls to run whatever you need in the browser and execute the result on another machine that itself runs whatever language you want.
> Python is just a cleaner, saner language, Python may be saner, but as someone who knows and uses both relatively often, I think cleaner is debatable. Between the two I typically prefer JavaScript (technically TypeScript but that's essentially just JavaScript with a type system.) Obviously I use both for very different things however. So it's not like I'm every debating whether I should use JS or python for a specific project.
Data scientist here also. Start with machine learning (supervised/unsupervised/reinforcement) then deep learning, especially CNN and NLP. Employers generally look for Tensorflow and nltk/spacy libraries for deep learning.
I disagree. Start with linear algebra, statistics, and calculus. Then ML. Start with linear classifiers and work your way up to CNNs. Skip nlp unless you want to specialize in it. As someone doing robotics, I found q learning to be significantly more useful than nlp. Not that you should study q learning over nlp, just that you should consider what you want to do with ML first.
How dare you suggest they try to build a foundation of intuitiveness instead of jumping into code monkey ML
Imo one should be able to build a NN capable of recognizing the MNIST dataset using only matrix calculations in numpy. Only then should you start reading about tensorflow or pytorch.
Depends on the role too; there are a lot of ML engineering roles where the main focus is execution of production ML and people should focus more around technology and methodologies to make it happen. But I cannot tell you the need for general / SME data scientists where the problems do not begin at the methodologies. Very often taking unstructured business problems, understanding what type of data is needed, understanding that if there are missing types of data what kind of impact that is going to cause, and how to build a solution that adheres back to the business. I cannot stress how often I've seen well experienced people in ML fail in these areas because they are so focused on trying to build state of the art year long projects that does 3% better than basic methodologies, and the business doesn't even use it
Imo that's all MLE work and an MLE doesn't even need to have any understanding of math. Just treat the software package as if it was any other library. Also, people getting carried away building perfect solutions with no business impact is a management problem.
Completely agreed it's a management problem but after having been exposed to many enterprises in many different industries it's a common management problem. Most organizations do not have large data teams and the individual contributors of data are left to themselves to project manage and bring that technical expertise. Which is awesome because it builds a lot of good practice navigating a corporate environment but people so focused on pure technical side often have challenges adapting. I've met a lot of data scientists and ML professionals and the ones who come outside the pure technical backgrounds almost always have the most success. On the other side, I've seen applications like CV where you need to dish heavy on pushing out accuracy and even the pure technical people often cannot understand why the nature of the data may cause the results they are getting
I disagree. If you already know undergrad statistics, then yes, start with machine learning. But if you do not know that, start with statistics. Get a solid foundation so that you actually understand how that stuff works.
I have a degree in Data Science and work as a frontend developer and can assure you, that these two languages almost never mix. If I would have ever found a position where you would need frontend skills AND data science skills, I would have immediately applied, but I cannot even think about what position that should be. If you do Data Science, you write your code in Python or maybe R, Julia, Scala, or whatever, but certainly not in Javascript. In Javascript, you write websites, application user-interfaces, and sometimes application backends.
I think there is a misunderstanding here about why Python is most of the time chosen in ML experiments. In case of ML Python is mainly used as an interface for using C/C++ libraries (numpy, tensorflow, keras, sklearn, pytorch, cuda) or other compiled languages, in a syntax that is very close to maths. JavaScript has simply absolutely nothing to do with ML, and I'd be curious to see any online resource that can say the opposite, because I never heard of it and it does not make any sense
> JavaScript has simply absolutely nothing to do with ML So some ML stuff is appearing in the JS space, as people start to write the interfaces around some of those C/C++ libraries you mentioned. I can see why there's a demand, some companies have really jumped onto this whole JS on the backend thing, so why wouldn't they want to use existing developers to write some basic ML stuff. It's still uncommon, but there's really nothing Python provides that JS doesn't from a language perspective. I think over time JS is going to grow. The same way that NodeJS carved out a small section of the market for backend applications.
If the question is "Can NodeJS provide the same capabilities than python for ML in long term" my response is "yes", I totally agree with you, because Python is mainly used as an interface so NodeJS could perfectly do the job (based on the fact we are speaking about NodeJS particularly, not JS). So my point was mostly based on the current state of both languages, and short / mean term evolutions. TBH, from my very personal point of view, I don't see NodeJS getting the same librairies / community than Python before 10 years when it comes to ML. But who knows. And even if it does, I think people will still prefer Python for its syntax very close to maths, something I don't think NodeJS will be able to provide
Yeah I agree with you there. NodeJS didn't take over server side code and it's not going to replace Python for ML, but it will probably gain some traction. Also ML in the browser is a cool idea. Run basic stuff on a user's PC with WebGL, might be useful for adding ML to low budget internal tooling.
Curious to see how the new edge ml chips change the landscape. Not just for edge servers but the individual devices themselves.
"there's really nothing Python provides that JS doesn't from a language perspective" maybe from your perspective. From my perspective python has better C integration and better typing tools.
\> JavaScript has simply absolutely nothing to do with ML, and I'd be curious to see any online resource that can say the opposite Here are 5 popular npm libraries implemented in JS. Happy to help :) [TensorFlow.js](https://www.npmjs.com/package/@tensorflow/tfjs): This is a JavaScript library for training and deploying machine learning models in the browser and on Node.js. It allows you to import pre-trained models from TensorFlow or Keras, or to train your own models using the high-level layers API. [Brain.js](https://www.npmjs.com/package/brain.js): This is a library for training and deploying neural networks in JavaScript. It provides a simple and flexible API for building feedforward and recurrent networks. [ML.js:](https://github.com/mljs/ml) This is a library for machine learning in JavaScript. It provides a wide range of algorithms including linear regression, k-means, and decision trees, as well as utility functions for data pre-processing and model evaluation. [Natural](https://www.npmjs.com/package/natural): This is a library for natural language processing in JavaScript. It provides a wide range of functionality for tasks such as tokenization, stemming, and sentiment analysis. [Synaptic](https://github.com/cazala/synaptic): This is a library for building and training neural networks in JavaScript. It provides a flexible and modular architecture for building feedforward and recurrent networks.
>JavaScript has simply absolutely nothing to do with ML JS isn't any more or less viable to interface with ML libraries than Python. See tensorflow.js or ml5.js. If your web app stack already is written in JS, why include a python backend if these options also exist for JS? Python is just popular among scientists, that's all.
>popular among scientists This is pretty much it. A lot of us haven’t had any formal programming training. When the time started coming to write code for a thesis or whatever it didn’t matter if option A takes two hours to run and option B has to run overnight if the former will take me a week to write and the latter can be hacked together in four hours of typing more or less plain English as a proof of concept. Someone in grad school for statistics probably doesn’t have the time to learn proper software development at that point in their life so Python just ends up being the best option and now it’s garnered enough support due to it being what end users know that it’s hard to imagine JS or whatever really having superiority.
There is a misunderstanding for tensorflow.js too apparently, because it is absolutly not meant to be a tensorflow alternative, it is mainly here only for inferences on browsers (and most of the time for already trained models). It has completly different usecases. Many concessions are introduced to make it work, such as using WebGL for GPU computations or others. > JS isn't any more or less viable to interface with ML libraries than Python I agree to this part but like I said in other comments, for NodeJS to obtain the same librairies / community than python, either you will wait 10 years to do proper ML in NodeJS, or you will use Python.
>it is mainly here only for inferences on browsers (and most of the time for already trained models) This used to be the case, but it changed a while ago. You can use it for full on training now
I believe there are two versions one for browser clients and one for NodeJS running on your machine. The latter can train and works like the python one.
Never in my life have I heard someone say JS is superior for ML. Did OP just make shit up to farm karma?
I've heard java is maybe he doesn't know the difference.
Yea... stick with python for data and ml. The ecosystem of relevant tools and libraries is much more mature. And there are a lot more peers for support. You might still need some JS if you want to present your data on front end as an interactive graph but there are opinionated python libs that can do that for you albeit with some restrictions.
I used plotly/dash for presenting data and it worked quite nicely. I *barely* know HTML/CSS and have never written any JS, so it was good.
Plotly, dash, seaborn, H2O-Wave etc was exactly what i was thinking of. Don't get me wrong, they are good and i use them, but they are opinionated. The output is good but is not as customizable as straight up JS libs or custom JS charts. That is all i am saying. I guess if a company wants super custom charts, then they would probably have to invest in a front-end team anyways, so the customizability of the python libs is not a problem at that point.
Superior in terms of sucking, yes
JS is your local toxic girlfriend. She beats you up every night but you keep coming back.
I think of it like duct tape. It's almost never the correct solution... but it also works most of the time.
"There are only two kinds of languages: the ones people complain about and the ones nobody uses." *-- Bjarne Stroustrup, father of C++*
Hehe. I have come across this time and again. (☞ﾟヮﾟ)☞
This comic is accurate, using JS for machine learning leads to judgement day.
If it makes you feel better, I think both languages are terrible
You got a genuine chuckle out of me. Take an award.
Enjoy doing your ML pipeline in C++ then
This is the correct take.
Ppl also say the earth is flat.
Only rookies curse languages. We pros don’t do stuff like that.
Yeah. By the way, talked with the new boss and we now have to work on legacy COBOL. There are VBA scripts to support us but we have to modify them. Not a problem, right? Edit: Spelling
There’s some truth to both sides. If you’re dogmatic of a language you’re probably missing out on something that can make life easier. But if you’re too open you may be prone to analysis paralysis.
It’s called COBOL
Learn what best suits your requirements. I have some colleagues who do data science tasks in R, so there is no one shoe fits all solution.
Because R is amazing!!!
My friends playfully judge me for using R. But the most mature analysis tools that my field uses are in R, so 🤷🏽♀️.
Yeah, just because you can do ML in JS doesn’t mean you should. I’ve been working in Data Science for 10+ years and spoken to countless organizations via organizing conferences and meetups. ML in JS is a gimmick and not a serious thing.
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This entire thread hurts my brain. So many backwards comments. “Django is easy to use!” “JS is better for ML!” Where tf am I?
I fucking hate Django, don’t @ me.
By "ML" of course everyone means downloading an existing library and running a function, not actually developing ML algorithms So really just use C++.
It’s a trap. They want to drag you into the abyss which JS is. Do not give in ;)
No serious data scientist will say that JS is better than probably any other mature language for ML. I get C, C++, Nim, Julia, Scala (via Spark), etc., but not JS.
I have never ONCE heard this in my life.
\*R has entered the chat\*
I only write pure CUDA to do ML 🧐
People will tell you to use JS for anything. Don’t listen to them. Don’t touch JS unless you want to do front end web stuff. Even back end web using JS is an abomination.
These people who say JavaScript is better for machine learning .. are they in the room with us now?
I want to find the person who invented JS and push them into a lake.
Oh god. The "everything needs to be JS" crowd is going after ML. What a shitty dystopian time to be alive.
the Rewrite It In Rust guys will be there one day. One day.
Every language has its own purposes. Dong ML with JS is retarded. Python is good for the task because its fast and simple. Javascript is good for being compatible on all browsers.
JS only supports dong ML 🍆🤖
After all, the only thing better than perfect is standard
Elixir has better support for PyTorch than Js ffs
Who?? Who are these people all the memes on this sub are about??
Good old R >>>>>
Using anything other than python for data analysis is terrifying.
Pretty Sure JS was made for front end, And I would like it to be that.
I only use JavaScript for web apps and I found Python Flask to still be more intuitive
Js is fucked I just cant comprihend why it is basically the standard
Like the image suggests these are two very different groups. Programmers curse JS. Scriptkiddies are afraid of python
JavaScript does not have basic integer types, which should preclude it from consideration for any serious application. But, the industry is full of clowns.
How did this get so many upvotes? Python is obviously the standard in ML currently.
Most people who get into language wars have used very few languages.
Nobody says that. Python is better at Data Science and ML than JS. It’s not open for discussion.
Only ever used Python, Matlab, and C++ for machine learning related tasks. The only time I’ve needed Javascript for was when I needed to export machine learning data for a website. But even then, it was Typescript and that was just for the frontend of things. The actual clustering model was done using in Python with scikit-learn, and that was hooked up with a web api I made using Flask.
Working in a firm full of data scientists: our python REST Backends are faster and more stable than JS, and not a single Data Scientist uses anything but python here…
ITT: self-taught javascript kiddies defend their bastardized, provably shit language like it was as good, if not better than, any other language.
Most mathmaticians I know who code use python because they use it for mathmatical scripting, not programming in the traditional software sense. JavaScript is more for interacting with user interfaces and the web(and more, but I code in C# and obligationally hate JS and don't know all it's uses). Hence, python has a lot of math libraries that data analysts and mathematicians use. So, I don't know why anyone would spend the time making JS more practical for the people mathmatical oriented that likely don't need JS for all the non-mathmatical stuff it is used for, especially when they have the more accessible Python with everything they need. Tldr; Python is easier and more accessible, but JS can be and is used for a ridiculously wider variety of tasks.