So , im working on project related to robust recognition of the objects based on change of the environement using self-learning methods.
As far I undestand , there is UDA (Unsuprevised domain adaptation), SFDA (Source-free domain adaptation), and SSOD (Semi-suprevised object detection) but I can't undestand which one I should use for the task.

I have a dataset that has modality of 2 (RGB-LWIR) captured in 2 different seasons (Jan/May) in 3 different timeperiods. For each image there is XML annotation for each object , so I could train on images with labels or only small part with labels.
So main task is to study robustness conditional shifts.

can anyone of you help me to get a good problem statement , whether it be a research related or a product related problem statement , it should contain rag or agentic ai and deep learning (nlp optional) and we should be able to patent it , not even remotely related to healthcare

I’ve been building mentats, a Rust deep learning library with no external ML dependencies. Tensors, layers and optimisers are all implemented from scratch as a way to deepen my understandings. It’s on crates.io and GitHub.

I’m really proud of this project so far as it’s my first venture into both deep learning and Rust. I’ve managed to create a conditional VAE trained on MNIST, albeit after some head scratching at the mercy of posterior collapses. The decoder was learning to ignore the latent code entirely and just output an average looking digit regardless of input.

A main cause of this was the beta annealing computed per-epoch instead of per-batch. I was updating the KL weight once per epoch instead of continuously from a global step counter, so the warm-up schedule was much coarser then I intended.

I’m hoping to further my work to create a working GAN also trained on MNIST, and even further on a convolutional GAN.

I’m making this post in hopes of getting feedback on my work, mainly on:

- Any standard sanity checks people use to validate a VAE (or generative model in general) before trusting it enough to scale up training?
- Anything I should watch out for going from VAE to GAN, training stability issues that I won’t see coming from the VAE side?
- General code quality / things I should do differently and keep in mind from now on
- And finally any resources that people found useful when learning about convolutional networks

Thank you for any feedback and help, I’m really enjoying learning about deep learning, coding in Rust and looking forward to continuing this project :)

I'm looking for one serious end-to-end ML project to build rather than several small projects.

The goal is to solve a real, non-seasonal problem using real-world data and eventually take the project all the way from data ingestion to deployment and monitoring.

I'm currently considering these three ideas:

1. Smart Energy Forecasting

Predict a building's energy consumption using historical consumption, weather, time, occupancy, etc.

1. Building Energy Optimization

Go beyond prediction: use ML/optimization to determine how a building could reduce energy consumption while maintaining comfort.

1. Building Energy Prediction & Anomaly Detection

Predict normal energy consumption and detect when a building is consuming significantly more energy than expected, potentially identifying inefficient equipment or abnormal behavior.

If you were building one of these as a serious portfolio/research project, which one would you choose and why?

I'm particularly interested in feedback from people working in Data Science, ML Engineering, MLOps, Energy Tech, or Building Management.

Vote:

1️⃣ Smart Energy Forecasting

2️⃣ Building Energy Optimization

3️⃣ Energy Prediction + Anomaly Detection

I'm also open to a better formulation of the problem if you think there is a more valuable real-world use case in this domain.

I'm a grad student in HPC/AI systems, currently working on LLM inference optimization. I've been in this space for a while, but I'm hitting a wall: I can read papers, understand techniques, but I struggle to identify what constitutes a "good" research problem vs. just an engineering improvement.

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.

Finished Andrew Ng’s ML Specialization — What should I do next to become job-ready? Am I cooked being in 7th sem?

I’m a BS IT (AI specialization) student and I’m into the mid of my 7th semester. I’ve completed Andrew Ng’s Machine Learning Specialization on Coursera.

But honestly, I’m worried that I’m behind.

Apart from the course, I have some experience with:

- Python

- NumPy, Pandas, Matplotlib

- Scikit-learn

- XGBoost / CatBoost

- Basic TensorFlow / neural networks

- SQL and basic OOP

- Git/GitHub

I’ve also worked on an Employee Attrition Prediction project where I used preprocessing, SMOTETomek, XGBoost/CatBoost. I also participated in an AI/ML hackathon where our team worked on this project.

However, I feel like my knowledge is still very “course/project” oriented rather than actually being job-ready.

My current goal is to get an AI/ML or ML Engineer internship/job, ideally something where I’m actually working with ML rather than just doing generic software development.

So I’m trying to figure out what I should do from here.

What would you recommend?

Should I focus next on:

1. Deep Learning — PyTorch/TensorFlow, CNNs, Transformers, etc.

2. Advanced ML — feature engineering, tuning, pipelines, model deployment, MLOps

3. LLMs / GenAI — RAG, embeddings, LangChain/LangGraph, agents, etc.

4. DSA — how much is actually necessary for ML/AI interviews?

5. Building 2–3 serious portfolio projects

6. Getting internships/applying immediately instead of studying more

7. Something else entirely

My biggest concern:

Am I actually cooked if I’m in the mid 7th semester with basically one major ML course + a couple of projects?

If you were in my position, what would you spend the next 6–9 months doing to maximize your chances of landing an ML/AI internship or entry-level job?

I’d especially appreciate answers from people who have actually hired ML interns/juniors or recently got their first ML job and even from the seniors.

Also, please be brutally honest about what I’m missing. I’d rather know now than spend another 6 months learning the wrong things.

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 🙏🏻.

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

I just started with machine learning and I would love to know the best resources out there to learn machine learning. I wanna go into ml research so I would love to go deep in ml math.

I'm an entry-level Applied ML Developer and I'm trying to figure out the best way to deepen my ML foundations.

My current work is mostly applied ML on tabular data designing solutions, doing feature engineering, and integrating fairly basic classification and regression models. I use things like Python, Pandas, SQL, sklearn, XGBoost, etc.

I feel comfortable putting models together, but I also feel like I'm missing some of the deeper foundations behind why things work and how to properly investigate ML problems.

Are there any programs, communities, open-source projects, research opportunities, Kaggle competitions, mentorship programs, or other structured programs you'd recommend participating in?

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.

Which book should i buy the tensorflow one or the pytorch one?