#learn computer science | Explore Tumblr posts and blogs

dreamdolldeveloper · 1 year ago

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back to basics

mostly free resources to help you learn the basics that i've gathered for myself so far that i think are cool

everyday

gcfglobal - about the internet, online safety and for kids, life skills like applying for jobs, career planning, resume writing, online learning, today's skills like 3d printing, photoshop, smartphone basics, microsoft office apps, and mac friendly. they have core skills like reading, math, science, language learning - some topics are sparse so hopefully they keep adding things on. great site to start off on learning.

handsonbanking - learn about finances. after highschool, credit, banking, investing, money management, debt, goal setting, loans, cars, small businesses, military, insurance, retirement, etc.

bbc - learning for all ages. primary to adult. arts, history, science, math, reading, english, french, all the way to functional and vocational skills for adults as well, great site!

education.ket - workplace essential skills

general education

mathsgenie - GCSE revision, grade 1-9, math stages 1-14, provides more resources! completely free.

khan academy - pre-k to college, life skills, test prep (sats, mcat, etc), get ready courses, AP, partner courses like NASA, etc. so much more!

aleks - k-12 + higher ed learning program. adapts to each student.

biology4kids - learn biology

cosmos4kids - learn astronomy basics

chem4kids - learn chemistry

physics4kids - learn physics

numbernut - math basics (arithmetic, fractions and decimals, roots and exponents, prealgebra)

education.ket - primary to adult. includes highschool equivalent test prep, the core skills. they have a free resource library and they sell workbooks. they have one on work-life essentials (high demand career sectors + soft skills)

youtube channels

the organic chemistry tutor

khanacademy

crashcourse

tabletclassmath

2minmaths

kevinmathscience

professor leonard

greenemath

mathantics

3blue1brown

literacy

readworks - reading comprehension, build background knowledge, grow your vocabulary, strengthen strategic reading

chompchomp - grammar knowledge

tutors

not the "free resource" part of this post but sometimes we forget we can be tutored especially as an adult. just because we don't have formal education does not mean we can't get 1:1 teaching! please do you research and don't be afraid to try out different tutors. and remember you're not dumb just because someone's teaching style doesn't match up with your learning style.

cambridge coaching - medical school, mba and business, law school, graduate, college academics, high school and college process, middle school and high school admissions

preply - language tutoring. affordable!

revolutionprep - math, science, english, history, computer science (ap, html/css, java, python c++), foreign languages (german, korean, french, italian, spanish, japanese, chinese, esl)

varsity tutors - k-5 subjects, ap, test prep, languages, math, science & engineering, coding, homeschool, college essays, essay editing, etc

chegg - biology, business, engineering/computer science, math, homework help, textbook support, rent and buying books

learn to be - k-12 subjects

for languages

lingq - app. created by steve kaufmann, a polygot (fluent in 20+ languages) an amazing language learning platform that compiles content in 20+ languages like podcasts, graded readers, story times, vlogs, radio, books, the feature to put in your own books! immersion, comprehensible input.

flexiclasses - option to study abroad, resources to learn, mandarin, cantonese, japanese, vietnamese, korean, italian, russian, taiwanese hokkien, shanghainese.

fluentin3months - bootcamp, consultation available, languages: spanish, french, korean, german, chinese, japanese, russian, italian.

fluenz - spanish immersion both online and in person - intensive.

pimsleur - not tutoring** online learning using apps and their method. up to 50 languages, free trial available.

incase time has passed since i last posted this, check on the original post (not the reblogs) to see if i updated link or added new resources. i think i want to add laguage resources at some point too but until then, happy learning!!

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when-we-begin-again · 8 months ago

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Yall math is fuckin easy

#i learned how to convert decimal to binary in like half an hour #computer science

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kaiasky · 2 years ago

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its not net zero information if u clearly fuckin know a new thing at the end!! net zero information meme is when the original post was untrue and so the only fact you've learned is 'it is not true that [untrue thing]', which is a useless thing to know (especially if you already knew the true thing)

if posts go back and forth and at the end u have a good idea what's going on, better than what you'd know from any post individually, that's not a time for 'net zero info'. u learned! we all learned! yippee

#kaia.mypost #this applies to #computer science #programing #as well! if u spend a week learning why an approach might not work that's still a well spent week

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alpaca-clouds · 1 month ago

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We need to talk about AI

Okay, several people asked me to post about this, so I guess I am going to post about this. Or to say it differently: Hey, for once I am posting about the stuff I am actually doing for university. Woohoo!

Because here is the issue. We are kinda suffering a death of nuance right now, when it comes to the topic of AI.

I understand why this happening (basically everyone wanting to market anything is calling it AI even though it is often a thousand different things) but it is a problem.

So, let's talk about "AI", that isn't actually intelligent, what the term means right now, what it is, what it isn't, and why it is not always bad. I am trying to be short, alright?

So, right now when anyone says they are using AI they mean, that they are using a program that functions based on what computer nerds call "a neural network" through a process called "deep learning" or "machine learning" (yes, those terms mean slightly different things, but frankly, you really do not need to know the details).

Now, the theory for this has been around since the 1940s! The idea had always been to create calculation nodes that mirror the way neurons in the human brain work. That looks kinda like this:

Basically, there are input nodes, in which you put some data, those do some transformations that kinda depend on the kind of thing you want to train it for and in the end a number comes out, that the program than "remembers". I could explain the details, but your eyes would glaze over the same way everyone's eyes glaze over in this class I have on this on every Friday afternoon.

All you need to know: You put in some sort of data (that can be text, math, pictures, audio, whatever), the computer does magic math, and then it gets a number that has a meaning to it.

And we actually have been using this sinde the 80s in some way. If any Digimon fans are here: there is a reason the digital world in Digimon Tamers was created in Stanford in the 80s. This was studied there.

But if it was around so long, why am I hearing so much about it now?

This is a good question hypothetical reader. The very short answer is: some super-nerds found a way to make this work way, way better in 2012, and from that work (which was then called Deep Learning in Artifical Neural Networks, short ANN) we got basically everything that TechBros will not shut up about for the last like ten years. Including "AI".

Now, most things you think about when you hear "AI" is some form of generative AI. Usually it will use some form of a LLM, a Large Language Model to process text, and a method called Stable Diffusion to create visuals. (Tbh, I have no clue what method audio generation uses, as the only audio AI I have so far looked into was based on wolf howls.)

LLMs were like this big, big break through, because they actually appear to comprehend natural language. They don't, of coruse, as to them words and phrases are just stastical variables. Scientists call them also "stochastic parrots". But of course our dumb human brains love to anthropogice shit. So they go: "It makes human words. It gotta be human!"

It is a whole thing.

It does not understand or grasp language. But the mathematics behind it will basically create a statistical analysis of all the words and then create a likely answer.

What you have to understand however is, that LLMs and Stable Diffusion are just a a tiny, minority type of use cases for ANNs. Because research right now is starting to use ANNs for EVERYTHING. Some also partially using Stable Diffusion and LLMs, but not to take away people'S jobs.

Which is probably the place where I will share what I have been doing recently with AI.

The stuff I am doing with Neural Networks

The neat thing: if a Neural Network is Open Source, it is surprisingly easy to work with it. Last year when I started with this I was so intimidated, but frankly, I will confidently say now: As someone who has been working with computers for like more than 10 years, this is easier programming than most shit I did to organize data bases. So, during this last year I did three things with AI. One for a university research project, one for my work, and one because I find it interesting.

The university research project trained an AI to watch video live streams of our biology department's fish tanks, analyse the behavior of the fish and notify someone if a fish showed signs of being sick. We used an AI named "YOLO" for this, that is very good at analyzing pictures, though the base framework did not know anything about stuff that lived not on land. So we needed to teach it what a fish was, how to analyze videos (as the base framework only can look at single pictures) and then we needed to teach it how fish were supposed to behave. We still managed to get that whole thing working in about 5 months. So... Yeah. But nobody can watch hundreds of fish all the time, so without this, those fish will just die if something is wrong.

The second is for my work. For this I used a really old Neural Network Framework called tesseract. This was developed by Google ages ago. And I mean ages. This is one of those neural network based on 1980s research, simply doing OCR. OCR being "optical character recognition". Aka: if you give it a picture of writing, it can read that writing. My work has the issue, that we have tons and tons of old paper work that has been scanned and needs to be digitized into a database. But everyone who was hired to do this manually found this mindnumbing. Just imagine doing this all day: take a contract, look up certain data, fill it into a table, put the contract away, take the next contract and do the same. Thousands of contracts, 8 hours a day. Nobody wants to do that. Our company has been using another OCR software for this. But that one was super expensive. So I was asked if I could built something to do that. So I did. And this was so ridiculously easy, it took me three weeks. And it actually has a higher successrate than the expensive software before.

Lastly there is the one I am doing right now, and this one is a bit more complex. See: we have tons and tons of historical shit, that never has been translated. Be it papyri, stone tablets, letters, manuscripts, whatever. And right now I used tesseract which by now is open source to develop it further to allow it to read handwritten stuff and completely different letters than what it knows so far. I plan to hook it up, once it can reliably do the OCR, to a LLM to then translate those texts. Because here is the thing: these things have not been translated because there is just not enough people speaking those old languages. Which leads to people going like: "GASP! We found this super important document that actually shows things from the anceint world we wanted to know forever, and it was lying in our collection collecting dust for 90 years!" I am not the only person who has this idea, and yeah, I just hope maybe we can in the next few years get something going to help historians and archeologists to do their work.

Make no mistake: ANNs are saving lives right now

Here is the thing: ANNs are Deep Learning are saving lives right now. I really cannot stress enough how quickly this technology has become incredibly important in fields like biology and medicine to analyze data and predict outcomes in a way that a human just never would be capable of.

I saw a post yesterday saying "AI" can never be a part of Solarpunk. I heavily will disagree on that. Solarpunk for example would need the help of AI for a lot of stuff, as it can help us deal with ecological things, might be able to predict weather in ways we are not capable of, will help with medicine, with plants and so many other things.

ANNs are a good thing in general. And yes, they might also be used for some just fun things in general.

And for things that we may not need to know, but that would be fun to know. Like, I mentioned above: the only audio research I read through was based on wolf howls. Basically there is a group of researchers trying to understand wolves and they are using AI to analyze the howling and grunting and find patterns in there which humans are not capable of due ot human bias. So maybe AI will hlep us understand some animals at some point.

Heck, we saw so far, that some LLMs have been capable of on their on extrapolating from being taught one version of a language to just automatically understand another version of it. Like going from modern English to old English and such. Which is why some researchers wonder, if it might actually be able to understand languages that were never deciphered.

All of that is interesting and fascinating.

Again, the generative stuff is a very, very minute part of what AI is being used for.

Yeah, but WHAT ABOUT the generative stuff?

So, let's talk about the generative stuff. Because I kinda hate it, but I also understand that there is a big issue.

If you know me, you know how much I freaking love the creative industry. If I had more money, I would just throw it all at all those amazing creative people online. I mean, fuck! I adore y'all!

And I do think that basically art fully created by AI is lacking the human "heart" - or to phrase it more artistically: it is lacking the chemical inbalances that make a human human lol. Same goes for writing. After all, an AI is actually incapable of actually creating a complex plot and all of that. And even if we managed to train it to do it, I don't think it should.

AI saving lives = good.

AI doing the shit humans actually evolved to do = bad.

And I also think that people who just do the "AI Art/Writing" shit are lazy and need to just put in work to learn the skill. Meh.

However...

I do think that these forms of AI can have a place in the creative process. There are people creating works of art that use some assets created with genAI but still putting in hours and hours of work on their own. And given that collages are legal to create - I do not see how this is meaningfully different. If you can take someone else's artwork as part of a collage legally, you can also take some art created by AI trained on someone else's art legally for the collage.

And then there is also the thing... Look, right now there is a lot of crunch in a lot of creative industries, and a lot of the work is not the fun creative kind, but the annoying creative kind that nobody actually enjoys and still eats hours and hours before deadlines. Swen the Man (the Larian boss) spoke about that recently: how mocapping often created some artifacts where the computer stuff used to record it (which already is done partially by an algorithm) gets janky. So far this was cleaned up by humans, and it is shitty brain numbing work most people hate. You can train AI to do this.

And I am going to assume that in normal 2D animation there is also more than enough clean up steps and such that nobody actually likes to do and that can just help to prevent crunch. Same goes for like those overworked souls doing movie VFX, who have worked 80 hour weeks for the last 5 years. In movie VFX we just do not have enough workers. This is a fact. So, yeah, if we can help those people out: great.

If this is all directed by a human vision and just helping out to make certain processes easier? It is fine.

However, something that is just 100% AI? That is dumb and sucks. And it sucks even more that people's fanart, fanfics, and also commercial work online got stolen for it.

And yet... Yeah, I am sorry, I am afraid I have to join the camp of: "I am afraid criminalizing taking the training data is a really bad idea." Because yeah... It is fucking shitty how Facebook, Microsoft, Google, OpenAI and whatever are using this stolen data to create programs to make themselves richer and what not, while not even making their models open source. BUT... If we outlawed it, the only people being capable of even creating such algorithms that absolutely can help in some processes would be big media corporations that already own a ton of data for training (so basically Disney, Warner and Universal) who would then get a monopoly. And that would actually be a bad thing. So, like... both variations suck. There is no good solution, I am afraid.

And mind you, Disney, Warner, and Universal would still not pay their artists for it. lol

However, that does not mean, you should not bully the companies who are using this stolen data right now without making their models open source! And also please, please bully Hasbro and Riot and whoever for using AI Art in their merchandise. Bully them hard. They have a lot of money and they deserve to be bullied!

But yeah. Generally speaking: Please, please, as I will always say... inform yourself on these topics. Do not hate on stuff without understanding what it actually is. Most topics in life are nuanced. Not all. But many.

#computer science #artifical intelligence #neural network #artifical neural network #ann #deep learning #ai #large language model #science #research #nuance #explanation #opinion #text post #ai explained #solarpunk #cyberpunk

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rigelmejo · 7 months ago

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learning to code!

When I was 9 years old, I learned enough html to code neopets pages, my own geocities websites, and I even made forums on my own sites so my friends could all roleplay together or rant together lol. And then? I forgot so much. I no longer no how to make a forum, or even a 'next page' button - so even the dream of just making a simple blog or webnovel site feels like a huge hurdle now. (9 year old me could probably figure it out in 2 hours).

So I'm relearning! I figured this would be a fun post to place resources I find for coding, since there's coding languages, and I figure maybe if you like running you're blog then you also might be interested in tools for making blogs!

First, for those of you who miss the old geocities and angelfire type of sites to make your own free site on: neocities.org

You can make free sites you can code yourself, the way 9 year old me did. A lot of people have made SUCH amazing sites, it's baffling my mind trying to figure out how they did, I definitely wish I could make an art portfolio site even a fourth as cool as some of the sites people have made on here.

And for those pressed for time, who aren't about to learn coding right now: wix.com is the place I recommend for building a site, it requires no coding skill and is fairly straightforward about adding pages or features by clicking buttons. I used it to make my art portfolio site, I am testing out using it for my webnovel - the alternative is Wordpress, but wix.com is letting me basically make a wordpress blog Inside my own site. It's very beginner friendly in terms of "how the fuck do I set up a 'sign up for updates' message and have my site actually email these people my novel updates?" and "I need a 4x20 grid of my art down the page, that lets people click the art to see it's information and make it bigger."

I did neocities.org's little html tutorial today, it's the part of html I DID remember (links, paragraphs, headers).

My next step is to go through htmldog.com's tutorials. They go from beginner, to intermediate, to CSS. Unlike many a coding tutorial I've seen, they explain what program on your computer you need to WRITE the code in and then how to save it and how to open it. (You'd think this isn't a big deal but I've been looking into how to learn Python for months and I can't find a tutorial explaining what fucking program to write my python in... notepad? do I need something else? I don't fucking know!! My dad finally gave me a printed textbook which supposedly tells you what to download to start... I learned C++ in college and for that you needed Visual Basic to code C++, so I figured I needed Something to Write the fucking python IN.)

#coding #rant #wooh my new CODING TAG #learning to code #i feel very. odd if im honest?#i genuinely knew how to build full fucking forum websites as a child including user sign ups #and i studied Computer Science Engineering in college so i did everything with C++ we were asked to and got As #and then i promptly BLOCKED IT OUT because i #HATED studying c++ SO fucking much. i hated my whole major. i did not like Engineering. i hated it. i was so mentally destroyed #by my college major that when i graduated i got a DIFFERENT job #and do NOTHING related to my major #i want to get into a more tech focused career eventually...since that is what my fucking degree is in #but i've been looking into something with less coding OR trying to teach myself #to like coding as long as its not fucking c++ again... i cant do it. too many bad memories #i think cybersecurity sounds like a fun job.#but u know me. im a person who likes knowing the BASICS #so i feel like i need to Relearn to code and learn python decently #before i try to study cybersecurity specific shit

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materialsscienceandengineering · 3 months ago

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How much/quickly do you think AI is going to expand and improve materials science? It feels like a scientific field which is already benefiting tremendously.

My initial instinct was yes, MSE is already benefiting tremendously as you said. At least in terms of the fundamental science and research, AI is huge in materials science. So how quickly? I'd say it's already doing so, and it's only going to move quicker from here. But I'm coming at this from the perspective of a metallurgist who works in/around academia at the moment, with the bias that probably more than half of my research group does computational work. So let's take a step back.

So, first, AI. It's... not a great term. So here's what I, specifically, am referring to when I talk about AI in materials science:

Most of the people I know in AI would refer to what they do as machine learning or deep learning, so machine learning tends to be what I use as a preferred term. And as you can see from the above image, it can do a lot. The thing is, on a fundamental level, materials science is all about how our 118 elements (~90, if you want to ignore everything past uranium and a few others that aren't practical to use) interact. That's a lot of combinations. (Yes, yes, we're not getting into the distinction between materials science, chemistry, and physics right now.) If you're trying to make a new alloy that has X properties and Y price, computers are so much better at running through all the options than a human would be. Or if you have 100 images you want to analyze to get grain size—we're getting to the point where computers can do it faster. (The question is, can they do it better? And this question can get complicated fast. What is better? What is the size of the grain? We're not going to get into 'ground truth' debates here though.) Plenty of other examples exist.

Even beyond the science of it all, machine learning can help collect knowledge in one place. That's what the text/literature bubble above means: there are so many old articles that don't have data attached to them, and I know people personally who are working on the problem of training systems to pull data from pdfs (mainly tables and graphs) so that that information can be collated.

I won't ramble too long about the usage of machine learning in MSE because that could get long quickly, and the two sources I'm linking here cover that far better than I could. But I'll give you this plot from research in 2019 (so already 6 years out of date!) about the growth of machine learning in materials science:

I will leave everyone with the caveat though, that when I say machine learning is huge in MSE, I am, as I said in the beginning, referring to fundamental research in the field. From my perspective, in terms of commercial applications we've still got a ways to go before we trust computers to churn out parts for us. Machine learning can tell researchers the five best element combinations to make a new high entropy alloy—but no company is going to commit to making that product until the predictions of the computer (properties, best processing routes, etc.) have been physically demonstrated with actual parts and tested in traditional ways.

Certain computational materials science techniques, like finite element analysis (which is not AI, though might incorporate it in the future) are trusted by industry, but machine learning techniques are not there yet, and still have a ways to go, as far as I'm aware.

So as for how much? Fundamental research for now only. New materials and high-throughput materials testing/characterization. But I do think, at some point, maybe ten years, maybe twenty years down the line, we'll start to see parts made whose processing was entirely informed by machine learning, possibly with feedback and feedforward control so that the finished parts don't need to be tested to know how they'll perform (see: Digital twins (Wikipedia) (Phys.org) (2022 article)). At that point, it's not a matter of whether the technology will be ready for it, it'll be a matter of how much we want to trust the technology. I don't think we'll do away with physical testing anytime soon.

But hey, that's just one perspective. If anyone's got any thoughts about AI in materials science, please, share them!

Source of image 1, 2022 article.

Source of image 2, 2019 article.

#Materials Science #Science #Artificial Intelligence #Replies #Computational materials science #Machine learning

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wronghands1 · 2 years ago

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#wronghands #webcomic #john atkinson #ai #artificial intelligence #computer science #science #machines #technology #robots #learning #robotics

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digitaldiariescs · 9 months ago

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Day 3: Beginning my first ever personal project

I'm honestly getting a little sick of beginning every post with, 'I continued working through the RWD course on freeCodeCamp', because that's pretty much all I do, every single day. And that's what I did today.

However, I started working on a personal project using HTML/CSS! Something that I realised today, as I started planning this project, is that I genuinely have no idea how to apply the knowledge that I've learned through the course. I know how to follow instructions and prompts, but I have no idea how to actually build a responsive web page. But, I think that's going to change as I start working on this project.

I'm trying to be as creative as possible with this project. I'm really going to challenge myself, and I hope this turns out the way I want it to. The main goal is to not take the easy way out with this project. The only way I'll learn is by facing difficulties.

Recently, I've noticed that I've been really struggling with creativity. I used to be an original thinker, but now, it seems as though I have lost the ability to think outside the box. And that is completely unacceptable in my world. Hopefully, this project will spark my creativity, while developing my coding skills.

With that, I shall take your leave and continue scribbling down all my ideas for this project onto my commonplace journal. Yes, my ideas have a real-world, physical home. I encourage you to keep one as well. It's a great way to build a relationship with yourself- bridge the gap between who you are and who you want to be, your thoughts and your feelings.

#html css #htmlcoding #css #computer science #coding diaries #coding #learning #studyblr #tech #computer #programming #technology #coder

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mandatory-blog-stop-asking · 1 year ago

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It's funny how most every cyberpunk story or setting thought that due to technology taking over people's lives and humanity, computer literacy would become commonplace enough that the very term would disappear. Everyone in Night City or whatever is super into hacking or can at least give you the difference between hardware, software, antivirus, spam, etc. To not know the basic gists or cybernetics and cyber security is paramount to not knowing how to count or how to read.

In reality we're about to enter an age where knowing how to create a folder or a zip file is back to being ancient lore inscribed in tablets that only the 30 year old who works at your IT office knows how to do. Phones and the growing marketability of easy-access no-customization technology means kids just don't use computers anymore. And it's crazy how fast it happened.

When I was in kindergarten we still had "computer class" once a week, and it was objectively useless for everyone in my class. Regardless of our age or interests, all of us had casual PC time either at home or in cyber cafes, all of us knew how to do things the teachers many times struggled with. The moment typing machine class became keyboard typing class, computers were already dominating most of our time. I learned how to navigate a computer the same way I learned English; by myself, because it was vital for my own interests.

And between highly streamlined video games, single umbrella closed OSs and everything being a fucking app, a 14 year old nowadays is lucky if they know what quotation marks do to your Google results. It's genuinely harrowing how the future is tech-dependent, yet we're becoming completely tech-illiterate.

The worst part is that it's completely on purpose by the tech industry. Much like not being able to fix your own products when they break, if you simply don't know what your phone or your computer can *do*, it's much easier to sell you a borderline identical one a little earlier than you'd actually need it. Phone updates are already pretty much semantic; you can't even see the difference between new models and old ones anymore, unless the visual difference is the point. And it all just gets more and more expensive for less and less bang for your buck.

We never expected the cyberpunk dystopia to be dull, and to rely on making us dumb. Crazy how well it worked.

#technology #tech #computer science #cybersecurity #these dang kids with their foortnoites and their moinecrafts #back in my day we learned to count with age of mythology #bit doom and gloom but seriously the amount of kids who dont know how to operate their own phones any more than boomers do is staggering #its all by design too of course

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