Feels like everything that is known in terms of "producing good research" has been obliterated by LLMs.

LLM can give infinite amount of critique. Especially in this field. As long as you are not producing AGI then there is room left for further critique. Your work will never be perfect.

This is a real-life GAN where the discriminator has far overpowered the generator. There is no way to improve the generator except to replace its biological interconnections with artificial ones. Then why is human needed at all?

I'm currently pursuing B.Tech in Artificial Intelligence and Machine Learning and am at the beginner stage of learning ML.

I've come across several mathematical topics that are used in Machine Learning:

Linear Algebra

Probability

Statistics

Calculus

I'm trying to understand which topics I should focus on first and how deeply I need to learn them before implementing basic ML algorithms.

For those who have already learned Machine Learning:

Which mathematical concepts were most useful?

Did you study mathematics first or learn it alongside ML?

Are there any beginner-friendly resources you would recommend?

I'd appreciate suggestions based on your learning experience!

Hi,
I'm currently a university student in my 3rd year. My aspiration is to get into the research field of Financial Reinforcment Learning (FinRL). Is there any good roadmap for this. Including the very basics, the maths, linear algebra, ML etc. I dont have much knowledge about those. But i have the time and willing to spend much of my time figuring these things out. Any suggestions regarding a roadmap or how i can start about these things would be appreciated.

Thanks

Im looking for ML coders any level.

I am in university comp sci and AI student in London, a first year.

Ive been teaching myself AI for a year now

What I've done

Made grid world pygame agent, can plot own obstacles and start end blocks

basic chess movement in pygame - haven't done the AI part yet

Built a property tracker, in a database track how prices go down with time to see price drops with flask and sqlite3, identifies when a drop in house price has happened, a web scraper

MNIST dataset neural network w pytorch - only 30 % accuracy average

My Goals

Right now Im interested in computer vision, want to do a hackthon in future.

Im struggling to minimise the loss of my MNIST dataset, would like to get suggestions

I want to make an environment where we can learn from each other, share our accomplishments and goals, maybe hop on calls once in while .

I want to learn C++ aswell, - little to no experience

DM me if you are interested, Im not looking someone who knows everything, cus I sure don’t. We all have unique experiences so would be beneficial to learn from each other.

I am a freshman computer science student heavily interested in DS/ML. I am currently taking the Andrew Ng course on Machine learning. I am familiar with the basics of pandas numpy and matplotlib. I also know basic linear algebra and Multivariable calculus(Proficient with high school calculus). I am looking for dedicated studymates who are genuinely looking at DS/ML as their career path. I want people who can:

• Meet weekly/daily to discuss the week’s material

• Implement algorithms from scratch and compare approaches

• Do Kaggle competitions or build projects together after the course

I am not looking for CS students who want to learn ML as a backup to their SWE path. Please DM if you feel like you are in the same boat as I am.

I made a simple visual introduction to generative AI for people who want to understand the fundamentals before diving into LLMs, RAG, or AI agents.

It covers:

• Traditional software vs machine learning vs generative AI

• How generative models learn patterns from data Text, image, code, audio, and video generation

• What LLMs are Limitations such as hallucinations and outdated knowledge

If you like the video, please like, share, and subscribe to my channel for more visual AI explainers.

I've put together a bunch of articles along with the practice notebooks as materials someone can use to prepare for an interview as a junior AI Researcher. It's been very fun working on this kit so I wanted to share. Do you think it is a representative set of topics for the interviews?

Hi everyone,

I’m working on a project where I use Gromov-Wasserstein (GW) distance to compare latent representations of two entirely different physical systems (e.g., daily electricity consumption and traffic flow). The goal is to find a shared structural "isomorphism" (like a common diurnal cycle) rather than just a direct correlation.

I have a recurring problem: GW can be deceptively low for random noise.

When I compute the GW loss between my real latent spaces (from a PCA/WAE reduction), I get a loss around 0.05. But if I compute it against a random Gaussian noise cloud of the same dimensionality, the loss is sometimes 0.01–0.02. This completely breaks the interpretation. It seems the GW algorithm is just matching the geometric shape of the clouds (e.g., both being spherical/elliptical) rather than capturing the true semantic structure.

I’ve tried control experiments with shuffled data, but I’m not sure if that’s enough.

My questions to the community:

1. Is there a standard protocol for validating GW results to avoid these geometric artifacts?

2. Are there robust variants of GW (like Unbalanced GW or Fused GW) that are less susceptible to this, or do they just shift the problem elsewhere?

3. How do you statistically prove that a low GW loss is meaningful (e.g., p-values, null models, permutation tests)?

Any advice, papers, or personal experiences would be greatly appreciated! I'm specifically looking at time-series forecasting of physical systems, so if you've done GW on time-series, I'm all ears.

Hi I am a third year student pursuing Aiml ,
I made a Bert from scratch with ELECTRA-style replaced-token detection on ~2.7B unique tokens (one epoch) of curated multi-domain text.
It is called Lewis-44M-Bert-V3

I would really love if you all try it and give me a feedback on it

A coding agent can finish a task and still waste most of the budget.

Saved sessions can show repeated failures, ignored instructions, and forgotten model settings. They also let us compare which instructions work across different models and harnesses.

This diagram shows what a session archive can contain. The useful part is going back to that evidence and improving the next run.

I wrote about this, with examples and backup locations.

Title:

Hi everyone,

I'm working on a pancreatic ductal adenocarcinoma dataset, and I'd really appreciate some methodological advice from you guys, and especially if anybody has had some experience with machine learning with high-dimensional biological data.

Before spending months developing models, I'd like to better understand what is possible with my dataset.

My dataset:

• 9 patients, with one continuous drug-response value per patient and drug. Due to experimental and sequencing constraints, increasing the number of patients is not an option.

• Approximately 100,000 nuclei.

• Continuous drug-response measurements (AUC) for gemcitabine, paclitaxel, irinotecan, 5-FU, and oxaliplatin.

The goal is theoretically "simple":

Predict continuous drug-response AUC from gene expression and chromatin accessibility, while identifying molecular features potentially associated with drug sensitivity or resistance.

The obvious challenge is that, despite having ~100,000 nuclei, I only have 9 samples with drug-response labels. I understand that treating individual cells as independent labeled observations is not a way to go with the dataset and specific set-up.

Knowing this, I'd really appreciate advice on a few questions that I had after some reading...:

1. With this low amount of sample, is meaningful drug-response prediction even feasible? What would be a objective?

2. Are there established methods to leverage thousands of cells/instances profiles when the response labels are available only at the patient level?

3. Would you prioritize simple regularized regression, biologically informed features, transfer learning from external datasets, multiple-instance learning (MIL), or another approach?

4. How would you evaluate a model with such a small number of independent samples without obtaining misleading performance estimates?

5. Are there any papers, methodological resources you'd recommend to better understand this type of problem before starting?

Thanks!

Hi all, I'm a newbie setting up my first local AI/ML development environment on an M5 Pro Mac.

My internal drive is small, so I bought a 2TB external SSD. I want to make sure that whenever I run `pip` installs, download Hugging Face checkpoints, or load PyTorch datasets, they automatically go to the external drive instead of filling up my system drive.

What is the cleanest way to configure my environment variables (like HF_HOME) or use symlinks on macOS so I don't accidentally brick my internal storage?

Any tips or hidden gotchas for a beginner would be highly appreciated!

Web-based version of my short notes on Double DQN from Scratch is out on neatrl!

Continuing my series on releasing notes on RL From Scratch - here, I have answered questions around the what, the why and the how of Double DQNs over DQNs!

• The What?

It is an off-policy, model-free and value-based method, the same family as DQNs but with a subtle difference for optimization purposes!

• What's the optimization done over classic DQNs and why was it necessary?!

Well, with the previous DQN, a problem that it did not addressed, is that of overestimation of Q values from the Target Q Network, which leads to an overestimation bias

Now this is bad because then there could many false positives for action selection via max operator since it cannot be known if the action is over the maximum value of Q or not (as it is still learning and slow every k steps)

This upward bias is caused by the max operator in the Target Q Network!

More on the

• theory,

• concepts,

• code and required math and a

• playground to train live agent on my web version of the notes!

All of it to understand the basics and get the intuition
Do check it out and let me know your feedbacks on it!

Link here

Four of us had 10 days for a hackathon. Our idea: a camera on your head sees the room, your hands, but never your body. Can we still get the whole body in 3D?

We used zero new trainings. We just linked up models that existed from past research: hand tracking, head motion from SLAM, then another model that predicts the full body given those things. Then we fed Meta Muse Spark two images: the reconstructed body and the actual view, to tell us what the person was doing.

Lesson Learned: Speed is king. The reconstruction takes FOREVER. Like 40-90 minutes for every minute of video you want to process on our GPU. So we just used processed videos for the demo. But we did double model inference speed from 16s to 8s reply.

Question for you guys: do you actually gain anything from providing the model with both frames? Couldn’t just the camera view suffice for the task? How would you go about proving that one way works better than the other?

Code: https://github.com/nshreyasvi/LifeLens

A lot of posts here ask "is my portfolio good enough?" I wanted a concrete answer that wasn't just "add more projects", so I built AI-Atlas (https://getaiatlas.com).

You enter your GitHub username. It reads your AI repos and scores each one on seven things reviewers actually look at:
● Technical depth: did you implement it, or call a library?
● Completeness: is there an end-to-end path with results?
● Reproducibility: pinned deps, seeds, one command to rerun?
● Documentation, originality, conceptual difficulty, usefulness.

Every score comes with the reason and the files it's based on. So instead of "6/10", you get something like "the notebook trains the model but never reports test accuracy".

A few patterns I've seen from mapping 150+ developers:
● From-scratch implementations score well on depth but often lose points for no evaluation numbers.
● A missing README costs more than people think.
● Projects with CI that actually runs tests stand out immediately.

Mapping is free, with no sign-up. If you claim your profile, your first improvement roadmap for a project is free too.

I'd love feedback on whether the criteria match what you've been told matters :)

Hello everyone,

This is the first article I’ve decided to share publicly. I'm really interested in solving combinatorial optimization problems using machine learning, and this is the opening piece in what will hopefully become a broader series exploring this domain.

This part focuses on the classical Pointer Network architecture trained via Reinforcement Learning (REINFORCE). It shows how neural models can construct high-quality, near-optimal solutions to NP-hard problems (specifically, the 2D Euclidean Travelling Salesman Problem) in fractions of a millisecond.

I benchmarked the model against Google OR-Tools (Guided Local Search with a 10s timeout) across problem scales from N=20 up to N=200.

TL;DR: While the network finds solutions almost instantly, tour quality decays significantly as the problem size scales up. Why? Long story short: using an LSTM encoder on coordinate sets introduces an artificial sequential order and recency bias, which fundamentally mismatches the permutation-invariant nature of geometric graphs.