@MattiaB0#scrapbook
This package is a tool for predicting the outcome of League of Legends matches. It uses data from the Riot API to train a machine learning model that predicts the outcome of matches based on various factors such as player performance, team composition, and game statistics.
Repo link: github.com/Neetre/LoL.git
@JackWachter7+Iteration2: I am continuing working on a large collection of CTF cyber security challenges enabling students or hackclubs to train CTFs. This pool includes a variety of challenges including stego, programing, OSINT, forensics, network analysis, crypto, and reveng. This is the completed second version.
github.com/JackIceHammer1/CTF-Pool
@ShiHaoNg5Writing this 20 minutes in: The previous bug can be seen below in the logs from Weights and Biases of the 2nd image. I'm using 4x Nvidia A100 to train my own custom GPT model, i.e. GPTesla. Previously managed to solve the issue of only 1 out of the 4 gpu running. Turns out i was using it entirely wrong, as in running the "Accelerate" framework completely wrong and messing upp the config files. Now I've also fixed an issue with only 1x of my AMD core being used, rather than all 96 cores being used. And it's working a lot faster now.
• now just trying to get it to train 50k steps.
github.com/Ice-Citron/GPTesla/commit/a105bbc2182d59f09b4bb8e3de6ab7682a2287c4huggingface.co/shng2025/gptesla-small/tree/robust-snowball-115 (115th run. Can be seen at Weights and Biases)
@Ultra0github.com/UltraArcher/password
Hopped on the train and made my own password generator using python. It uses the os.urandom function which I believe is "more random" than the random libraries, also it generates random numbers and then ASCIIifies them to get the characters, which I think I could possibly improve in the future.
@Bartosz1Finally finished my Sprig game! It's a RPG where you train your SpriMon. What's the best? It's Arcade event themed!
If people will want, I can make it even more advanced, add catching mechanics and more. I would love to make it a full game with much more details and a better, far more longer story in a Game Engine. It was a fun way to learn JS for the first time!
Here's repo: <https://github.com/BudzioT/SpriMons/tree/master|https://github.com/BudzioT/SpriMons >
Play it here: sprig.hackclub.com/share/4KvmUylH6Wtw3dCh9Er3
@Z1Here's a library I made that helps you use MTA data: github.com/ZarmDev/transitHelper
So far, you can get the coordinates of all the train stops, get the train line shapes (to show on the map) and get the arrivals at a specific stop.
@pro-grammer0Here is the finished version of the sentiment analisis model I have been working on!!!
• This is the actual code for the model:
github.com/adrirubio/Perceptron/blob/main/sentiment_analisis_model.py
If you train that model you get a trained and saved model ready for usage this model adds to the previously created transfer learning model for talking, image recognition and soon to be created image detection model!!!
• Here is the code for Perceptron with the models we have already discussed:
github.com/adrirubio/Perceptron/blob/main/perceptron.py
• And here is the home page for Perceptron:
github.com/adrirubio/Perceptron
For those of you who don't know what I am creating it is basically a combination of many AI model and it is called Perceptron a model that can talk to you (the transfer learning model) only triggered by its name (Perceptron) and it also has manual resposes here are some examples: plays music, stops music, searches on google, searches on the wiki, gets the time, gets the weather and more...
Apart from the transfer learning model it also has an image recognition model that you can also train and when you say the word "image" when you talk it asks for the path and predicts what the image is!!!
The latest model (the one I have just created) is a sentiment analisis model that when you talk to Perceptron after it responds it (the sentiment analisis model) also tells you its prediction for how you are feeling example : happy, sad ...
All of these models are really cool but believe it or not there are still more comming!!!
For more information on Perceptron read the readme at: github.com/adrirubio/Perceptron
I really enjoy this project and think it makes a good combination of several AI's two for talking (the transfer learning model and the sentiment analisis model) and two for image recognition (image recognition model and the yet to come image detection model)!!!
With that I leave you to enjoy the journey I had to create the sentiment analisis model (probably my hardest model yet!!!)
@jeslyn0Finished a video edit dedicated to Bullet Train! I love the brothers. Watch it here. (github)
@pro-grammer0This is the finished version of Perceptron for now but I will modify Perceptron in future sessions:
• Home page and readme:
github.com/adrirubio/Perceptron
• Transfer Learning model:
github.com/adrirubio/Perceptron/blob/main/model.py
• Example of how the Transfer Learning model might generate responses:
github.com/adrirubio/Perceptron/blob/main/generate_responses.py
• And the actual code for Perceptron the manual if statements for features and the conversation model:
github.com/adrirubio/Perceptron/blob/main/perceptron.py
If you train the model and run the Perceptron code you should have a fully functioning conversation model with some cool features and you can run this code using a cron job and have Perceptron running on the background of you computer and only be trigged by using the key work Perceptron. For more information see the readme of Perceptron.
But, Perceptron doesn't finish there I am planning to create a CNN model that will add functions to Perceptron.
So, I will be back...
@PraneethMandalapu0I wanted to create an app for peoplesense.ai, they have train occupancy sensors, and I thought it would be cool if I adapted it for my local train system, ACERail, and made a neat little app for occupancy reporting.
Link to Figma:
view
@Matthew-U05BA68DB6E0I made some pretty big updates to the 3d train model. All the files can be found at github.com/jedim101/train, along with assembly instructions for printing on your own. It is now much easier to assemble and has customizable text!
@EddyZhao0I started making my MBTA Train Tracker app! The goal of the app is to tell people upfront and quickly when their nearest MBTA (Massachusetts rail system) commuter rail train will reach their station. It uses Geolocation and IP to figure out the user's location, finds their closest train station, and then scans the nearby stations to see if there is a train coming. Then it will predict the time the train arrives and report its distance. Here's what it is so far (there is no CSS, but that will come later). I've gotten the API calls and location tracking to work.
@JoshuaRodriguez0Currently Reading: Deep Learning - John D. Kelleher
Transfer learning is the idea that neural networks don't need to be trained from scratch every single time we have a new problem to solve - we can instead just create a general network, which solves the problem we're trying to solve pretty well, and then train it on a small dataset of information specific our problem.
One way that BERT (A new type of language processing neural network) utilises transfer learning is through self-supervised learning. The model takes a dataset of unlabelled sentences (so, we haven't quality checked it, or made our own questions to train the model on, or anything like that), hides part of the sentence, and tries to guess what should fill it in - then updates the network accordingly. Then, the network is trained very specifically on a small dataset of sentences relating to medical diagnosis, natural speech, jokes, etc etc.
The interesting thing about BERT's use of transfer learning is that it's not just using it to decrease the energy required to train future language processing networks - it's also doing it so that it doesn't need a large labelled dataset - it just needs a whole lot of unfiltered human garbage words, and then a small set of filtered, specific words. This makes data collection for training a neural network so much quicker!
@dav0Day 3 of #10-days-in-public
Experimented w/ the obstacle spawning logic in a way that 1. makes sense and 2. doesn't make the game impossible. The result is worse than what I had yesterday, but hey its progress! Added trains as well, but currently each cube is drawn individually which terrible for performance. Unfortunately, the sprig can only handle ~18 cubes with my crappy rasterizer. Ideally, I should treat each train as its own cube rather than a serious of cubes like I do now. Not only would be it more performant but also easier to reason about when it comes to spawning logic. But anyway, don't feel like working anymore so that's for tomorrow me :p
@sam0I've been forgetting to write the past few days so here it comes! The past 4 days @ Shad :shad: and we're nearing the end of the program, here's some highlights! The final presentation went well as expected! We also went to 🇨🇦 Banff! It was my second time being there. The view in general, especially 🏞️ Lake Louise, was gorgeous as usual and I had some amazing 🤌 ramen at the Town of Banff! The same day, we got to ride the Banff 🚡 Gondola. The view during the ride and the views on top of Sulfur Mountain ⛰️ were amazing. I had a great time with my fellow Shad members. We also went to downtown 🏙️ Calgary! Took the C-Train 🚊 again; I love Calgary Public Transit. Visited the top of Calgary Tower, the view was breathtaking.
That's all! With only a day left in the program, and all the memories and connections I've formed, it feels bittersweet that I'll probably never see the majority of them after a day.
@sam0Another tiring and full Shad :shad: day! More 🏛️ U of Calgary and the famous 🐴 Calgary Stampede!! Visited the UofC School of Architecture, Planning + Landscape. Saw some very cool student-made projects as well as their workshop! Also took the C-Train there and back. It was really crowded. Stampede wise, the food 🍟 there was surprisingly good (which is why I forgot to take pictures oops). Had some wacky stuff like ketchup and mustard 🍦 ice cream.
@Peiprjs0No I did not hack into the train screens just to make that shitpost-looking thing appear… this 2000’s “Next station” screen is the one actually used in the S-102 RENFE train series.
@Peiprjs0Train and book time! I’m on a 449 train as usual
@Peiprjs0I’ve been working on a drawing for a friend. Currently I’m on the train, working on it.
@Peiprjs0In the train. Still haven’t even had a coffee and I’m already falsifying processing data
@linkai0Day 1/10 of #hardware-party!! :winter-hardware-wonderland:
My project is a split-flap display, like those old black and white displays you see at train stations and airports back then! I got all of my parts, assembled my 3d printer, and already have been working on remixing some cad designs. Through many attempts (and many failed prints!), here’s what I got for my display so far. I’m gonna start playing around with motors and hall-effect sensors next! Also, I’m still in the process of 3d printing all the flaps 😁
@aaryan0Yo! It’s been a minute since I posted here, but I just hopped on the Z train and I gotta get my groove on with Go. So I’m starting with this fresh book: “Learn Go with Tests”, it’s showing me the ropes of Go with a TDD twist.
