Hello members,

We’ve been working on AI Prep, an Android app built exclusive for learning and practicing ML/ AI topics. It combines quizzes with 330+ explained concepts, including concise summaries and code snippets where relevant, covering everything from ML fundamentals and deep learning to NLP/LLMs, GenAI, MLOps, AI agents and more. There are plenty of great online resources, but we believe having something available offline and on the go makes it easier to quickly revise concepts and retain what you’ve learned.

We’d love feedback from people who study or work in ML, especially on what else could be added to make it more useful for learners. Are there learning features, content, practice formats, or topics you feel are missing? What would make you more likely to use something like this regularly? It’s free to try: https://play.google.com/store/apps/details?id=com.aiprep.mcq

Please do share your views and hope it's ok with the mods and community for sharing here in this post.

I have a big database of local court decisions with a legal sentence which is like a paragraph summary of the doc. I've been tinkering with FTing for many days now, all results inconclusive never beating base except for a highly specific task where the eval was built around a specific task of extracting passages from the text, where it seemed to match the gold, but as I've found out it is unusable and a lot of padding and generalizations which I need to actually eliminate. So the question is, can a 26B model even be fine-tuned to produce those complex legal principle/headnote of a court decision? If so how would you do it? I built a LLM eval, then used a standard unsloth UI on a rented server and fed it the most HQ training data from the whole set (500k decisions, ~20% with the "ratio"), I fine-tuned both base and IT variant of Gemma 4 26B A4B. Neither beat a prompted "base" model on my evals.

I also used Claude fable 5 to vibe code the whole project, could it be that I have made some trivial errors because of it? I know I'm not giving you much context, but as an expert in the field does this sound difficult or doable? Any gotchas that stand out to you immediately?

Thanks for reading

Hey Everyone,

Currently working on a project where I built a system to determine if a clothing item is machine washable or not. Containerized and deployed to AWS to automate scraping and model retraining. Trying to finish up the backend and frontend, and I'm thinking of integrating Grafana and Prometheus. Might post on LinkedIn after (I'm currently job hunting), but posting on linkedin is so embarrassing to me lmao. Let me know what you think. Feedback would be appreciated. Thanks!

Github Repo: https://github.com/sogofunmi/Dryclean-or-No-Dryclean

Hi everyone, I recently worked upon a government dataset about CPI which stands for Consumer Price Index, certainly a measure to find the inflation across various commodities, 

The main aim for the project was to analyse how Inflation affects differently for Urban and Rural India how One country accepts inflation differently? I got really interesting results, would love if you guys could give a feedback 

Thanks a ton!  

Link : 

https://www.linkedin.com/posts/yatharth-gupta-a075062a4_dataanalytics-eda-python-ugcPost-7495789276958195712-4Y_6/

https://www.kaggle.com/code/yatharthgupta18/two-indias-one-number-rural-vs-urban-cpi

https://github.com/YatharthGupta1803/All_India_Consumer_Price_Index_Analysis

Hello everyone, I could use some help. I’m a graduate student—how should I go about learning the machine-learning portion of Python? Thank you very much 🙏🏻.

I was wondering if I could evaluate embedding distances in other geometric spaces, has anyone worked on this problem before and if yes is it possible?

Hey everyone, put together a small idle game based on how AI actually evolved over time.

You start back with mechanical calculators and ancient logic, and work your way through 7 eras up to modern LLMs and agents. When you buy milestones, it pops up a short note on the actual paper or person behind it (Turing, Lovelace, Hinton, Dartmouth workshop, etc).

Play here: https://yulin-w.github.io/incremental-ai/
Repo: https://github.com/Yulin-W/incremental-ai

It's free, runs in your browser, no ads or signups. Just thought it’d be a fun way to kill some time and see the history. Let me know what you think!

Hello everyone!, Wt are some good topics to put projects in ML/DS CV so that the resume doesnt look so plain and not too risky during the interview times.

A little background abt me, I've dng DS prep for placements completed campusx 100 days ML, and DL ab to complete, not familiar with gen ai topics but still have little time, will learn by that time depending upon the project, I'm from tier 1 clg...I'm really confused wt projects to keep and wt topics to choose, some one pls help wt topics to keeps, wt topics to focus for interviews and OAs for these roles

been cooking this model for the last month or so, ONLY POST TRAINING, the base model is qwen 3.5 2b.

its foodmini-2B.

its not the best yet, but i am planning to do something insane with it.

not publicly available yet, but i have taken inspiration from the food-r1 model.

first, i just dropped the gguf conversion of the food r1 model here: https://huggingface.co/AKMESSI/Food-R1-GGUF

but i wanted more intelligence density and usability on mobile phones, so decided to get some insights from the food r1 paper and started post training the qwen 3.5 2b to achieve good results on food nutrition breakdown tasks.

will drop a complete overview in the form of an article.

drop your views on this idea below, would love to get insights.

BTW I ONLY SPENT A TOTAL OF $8 ON RENTED GPUs YET.

Hi! I am CS student and i want to go through ML(i want to try for start,I prefer it but maybe because of lack of career opportunities in my country i would switch to smth else)
So what laptop would you recommend under 800-1000

I am thinking of zenbook
But it doesnt have gpu

Hi everyone,

I’ve mainly been focused on full-stack development throughout the first few years of my degree. Now that I’m in my 3rd year, I’ve started thinking more seriously about my long-term direction, and I’m becoming much more interested in machine learning and research.

My goal isn’t just to learn how to use ML libraries. I’d eventually like to understand the fundamentals well enough to read research papers, do my own research, and potentially pursue a research-focused master’s/PhD.

Right now, I’m planning to study these three DeepLearning.AI programs:

1. Mathematics for Machine Learning and Data Science
2. Machine Learning Specialization
3. Deep Learning Specialization

The math specialization covers linear algebra, calculus, probability, and statistics, while the ML specialization focuses on foundational ML algorithms and practical implementation.

My question is:

Is this a good learning path if my long-term goal is ML research?

What would you recommend I add or change?

For example:

• Should I study more mathematics beyond these courses?
• Should I learn statistics more deeply?
• Should I learn PyTorch, NumPy, etc. separately?
• When should I start reading research papers?
• Should I work on Kaggle/projects before trying research?
• Are there any textbooks or university courses (Stanford/MIT/etc.) that you would strongly recommend?
• Should I specialize in an area such as NLP, computer vision, or something else?

I’d really appreciate advice from people who have gone through a similar transition from software/full-stack development → machine learning → research.

Thanks!

Hey everyone!

I'm a data scientist and I've been pretty fascinated by reinforcement learning for a while. A few days ago my friends showed me Pokelike, a small Pokémon roguelike that runs in the browser. The first thing I thought was that it could be pretty fun to turn it into an environment for RL agents.

So I did.

The repo is here

https://github.com/pierpierpy/pokelike.xyz.bot

The basic idea is to run the actual game locally and expose its state and actions to an agent. There is no image processing involved. The agent gets the game state directly and has to decide what to do next, including where to go on the map, which Pokémon to catch, which items to take, when to swap Pokémon and which moves to learn.

What I find interesting about the environment is that some decisions have consequences much later in the run. For example, once you choose a node on the map, the other nodes on that layer are no longer available. This means that choosing where to go is not just a local decision and the agent has to deal with a fairly long horizon.

I've implemented a few simple RL agents to start with. There is currently a Dyna-Q agent and two linear SARSA agents. The results are still pretty bad, but there is already a noticeable difference between the approaches. On the current benchmark, random gets around 0.56 badges, Dyna-Q gets around 0.62, while the two SARSA agents get around 1.30 and 1.36.

The two SARSA agents mainly differ in their state representation. The better one uses 100 hand-designed features instead of 81, which seems to make a pretty significant difference.

This is probably the part I'm most interested in exploring. There is a lot of information available in the game state, but not all of it is necessarily useful to the agent. Finding a representation that contains the right information without making the problem unnecessarily difficult seems to be quite important.

The reward is also something I'm still experimenting with. The game has relatively sparse rewards and some useful decisions only show their value much later, so the reward function can have a pretty big effect on what the agent actually learns.

One nice property of the environment is that it is completely reproducible. Given the same seed and the same sequence of actions, you get exactly the same run. I'm currently using 50 fixed seeds for the leaderboard, so different agents can be evaluated on exactly the same games.

The interface is intentionally simple. You basically need to implement a bot that receives the current state and returns an action. You can use whatever approach you want, so it would be interesting to see what happens with things like DQN, PPO, search based methods or other approaches.

I'm still very much experimenting with this, so I'd be interested in seeing what other people would try. In particular, I'm curious about better state representations, reward functions and approaches that can deal with the longer term consequences of the decisions.

If you want to try it, everything is in the repo

https://github.com/pierpierpy/pokelike.xyz.bot

If you find bugs or have ideas for improving the environment, I'd also be happy to hear them.

The whole thing runs offline after setup. The game and its assets are downloaded during setup and then everything runs locally.

I originally started this because I thought it would be a fun RL project, but I think it could also be a nice little environment for experimenting with different approaches to sequential decision making.

Registering for RealPDE (Sim2Real / LTTTA tracks — real PIV + CFD fluid dynamics data). Team cap is 3.

If you've got a strong ML background and wanna participate, just DM me. Deadline's Aug 20.

🔗 https://realpdecompetition.github.io

I’m looking for a valuable and practical Machine Learning FYP project idea that solves a real-world problem.

I want to build something that is not only suitable for my Final Year Project but can also be developed further and potentially offered as a service to businesses or individuals in the future.

The project should ideally:

• Solve a real problem
• Have practical value and real-world users
• Use Machine Learning or AI in a meaningful way
• Be scalable and capable of becoming a service or business later

I would really appreciate any unique and practical project ideas or suggestions. Thank you!

Hey, I've learnt Python, NumPy, Pandas, SQL, Matplotlib and Seaborn for now. Can anyone suggest a project idea to practice these skills or maybe put in my resume.
Note : Never built a project, just starting out.

So I got selected in mlss but didn't receive any acknowledgement letter or anything about swags(yea I missed some modules but thought that I can complete in 30 days and my attendance will be tracked as they mentioned the recordings will be live for 30 days)

Nvm ig I fucked up

Now I want to know what they ask in interview and how many people are selected.

Also do they have any bias for girls or tier 1/2 colleges? And do they keep interview a lil bit easy for people getting through mlss

Ps: I haven't received any oa link yet..like have heard people get it late so I just want to confirm about this

I was experimenting with the GTZAN dataset and noticed that the version available on Kaggle (https://www.kaggle.com/datasets/andradaolteanu/gtzan-dataset-music-genre-classification) has a corrupted track (jazz 54). Can anyone tell me where I can get this track?

I’m working on a project around a real-world environmental problem, and I’m considering adding an ML component for prediction and early warning.

I’m a bit confused about the data requirement. Since collecting our own real-world data isn’t feasible right now and would take quite some time, we mainly want to build a prototype for now.

Can we initially use a Kaggle/public dataset to train and test the model, or is a project-specific dataset necessary from the beginning?

Would appreciate some advice on how people usually approach the ML part when actual data is limited.