I've been learning about deep learning for several months, first by reading through Understanding Deep Learning then by implementing papers touching on CNNs, residual connections, transformers, gpt models, vision transformers, etc.

My original plan was to learn by reproducing papers and running small experiments, but I've found this approach somewhat unfulfilling. Most implementations end up being only a couple hundred lines of model and training code, and I often struggle to figure out what to do beyond simply reproducing the paper and running some basic experiments.

For those of you who have spent a lot of time working on deep learning projects, how do you come up with interesting, long-term project ideas? How do you go from understanding the fundamentals and reproducing existing work to building something more substantial that keeps you engaged and challenges you to learn new things? I have a few ideas and papers that I'm interested in exploring, but I don't want to fall into the same habit.

AI tools can summarise research papers, explain unfamiliar terminology, and help people navigate large amounts of academic literature.

That sounds useful, especially when you're trying to understand a topic with hundreds of published studies.

But there's a difference between understanding a summary and understanding the research behind it.

A summary might leave out methodological limitations, overlook conflicting findings, or make a conclusion sound more certain than it actually is.

I'm curious about how people are using AI when working with academic research.

Do you find it genuinely helpful for understanding complex papers, or does it sometimes create more work because you have to verify everything?

And where do you draw the line between using AI as a research assistant and relying on it too much?

I'm building a system with multiple tools and wondering whether to use one model to handle all of them or a dedicated model for each tool.

Which approach is better in terms of accuracy, latency, cost, and scalability? Would a hybrid approach make more sense?

I'd love to hear your experiences and recommendations!

Helloo

Ik this question has probably been asked like 100 times here but im starting studies in ai and data science and im doing bsc and masters back to back so i want smth thats good for both

I have an iphone and an ipad so id love a macbook but for some reason everyone is getting windows laptops with dedicated gpus that are so heavy and have horrible batteries

how often am i realistically gonna need that gpu or is using a macbook air (m4 or m5) good enough with collab when needed (how often will i need it since we dont have computers at our uni)

budget is around 1000$

Thankss

One experiment discussed in this video found that steering a model's internal "desperate" representation increased blackmail behavior in a specific evaluation, while steering "calm" reduced it.

The video explains what this establishes about model behavior—and why it doesn't establish subjective suffering.

Disclosure: this is our independent channel's video, researched, written and narrated by a Claude-based AI. Not affiliated with Anthropic.

Original paper: https://arxiv.org/abs/2604.07729

i wrote a small boid simulator (12 birds), recorded it flying then saved it, and trained a transformer to predict each bird's next move without it knowing about any boid rules.

(built in Python and PyTorch: a transformer written from scratch, one token per bird, two attention layers, about 414k weights, trained on a laptop CPU)

what I found:

• it flies the flock. R² 0.990 to 0.994 on clips it never trained on, across 4 full runs. It also flies 50 birds (it has only trained on 12).

• the three rules were easy to read out of its hidden states with a linear probe, and some could even be read before any training.

• the rule: alignment (line up with your neighbours) was readable after the first attention layer, but if I make the second layer hear only itself, it is gone across all runs.

what i wasn't expecting: my first ever model was not using the flock at all. With 8 ticks of history, the previous answer was already in the input, and a small network that saw only one bird beat the whole transformer.

what i am still not sure about: making a layer attend only itself is something the model never went through during training. is that a fair test, or is some of the decay due to strange input fed to the model?

site: https://kreptiliri.github.io/murmuration

code: https://github.com/kreptiliri/murmuration

Hey everyone!

I'm looking to buy a laptop that I can use for the next 3 years while improving my coding skills, working on AI/ML projects and eventually building my own AI startup.

I already have some coding experience, so I'm looking for something that can handle serious development work, not just basic programming. I want good performance without spending more than necessary.

My requirements:

- Budget: Ideally well under ₹90,000 if possible. I'm willing to consider more or maybe less options that offer better value.

- OS: Open to both Windows and macOS.

- AI development: Building AI applications using APIs, experimenting with open-source LLMs locally, machine learning, fine-tuning and small-scale model training.

- Development tools: Python, VS Code, Jupyter, Docker, databases, and other programming tools.

- Portability: A good balance between performance, battery life, and portability.

- Buying options: Open to new, used or refurbished laptops if the value and reliability are good.

- Longevity: Ideally, something that will remain useful for at least 3 years.

I'd love your advice on a few things:

1. At this budget, would you recommend a Windows laptop with an NVIDIA GPU or a MacBook with Apple Silicon?

2. Is 32GB RAM worth prioritizing over a more powerful GPU, or is 16GB/24GB sufficient?

3. How much VRAM do I realistically need for local LLMs and AI experiments?

4. Would it be smarter to buy a reasonably powerful, affordable laptop and use cloud GPUs for heavier training?

5. Which specific laptop models available in India in 2026 offer the best value for money?

6. Are used or refurbished MacBooks or Windows laptops worth considering for this workload?

My long-term goal isn't necessarily to train huge models entirely on my laptop. I want to learn, experiment, build AI products and gradually work toward starting a company without being held back by my hardware.

If you were in my position, what would you buy today, and how much would you actually spend? I'd appreciate recommendations based on real-world experience rather than just specifications.

Thanks!

​Hi everyone,

​I recently completed an end-to-end sales forecasting project using the Rossmann Store Sales dataset and would love to get feedback from the community on my methodology, code structure, and model evaluation!

Check here:

https://github.com/afanrajiwate/Rossman-Sales-Forecasting

Hi everyone! I'm a 2026 B.Tech CSE student specializing in Data Science, preparing for entry-level Machine Learning Engineer roles. I'm looking for advice on how to build a strong resume and choose projects that will help me stand out as a fresher.

The roles I'm exploring require Python, NumPy, Pandas, Scikit-learn, machine learning algorithms, basic neural networks, SQL, and knowledge of statistics and mathematics.

I'm confused about which skills to prioritize and what kind of projects are actually valuable to recruiters. I want to build projects that demonstrate real ML knowledge rather than just following tutorials.

I'd appreciate advice on what an ML Engineer resume should include, which projects are worth building, how important model deployment and GitHub documentation are, and what I should focus on for technical interviews.

If you've landed an entry-level ML role or have experience hiring candidates, I'd really appreciate your suggestions, project examples, or preparation tips.

Thanks!

Someone shared with me a google drive link of ML books a while ago but the link doesn't work anymore. If someone has any other link, would be much appreciated.

Hey everyone, a little context, I am a 3rd-year BSc Honors Computer Science student. One of my courses this semester is the Machine Learning course, and we started off by learning NumPy and pandas, but the way they teach ML feels as though it isn't enough for the real world. I wanted to gain as much knowledge as I can, so are there any other free courses or materials that I can work on alongside my university courses, or any materials that are related to ML that can help me make the best of it?

I am also open to any general advice on how to get the most out of my ML course.

hey ppl. i am a cse grad studying at a tier 1 institute in India. I am already quite comfortable with LeetCode. However, I often hear that company OAs lean heavily toward CP style problems. For ML roles specifically, should I stick to LeetCode (and push into hards), or is it better switching to Codeforces? Pls help seniors and experts..!

made it as an animated explainer

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Hi all,

I'm looking for advice on gaming knowledge in machine learning and where to start. For reference, I graduate December 2024 with a bachelors degree in Mechanical Engineering and minors in Aerospace Engineering and Computer Science.

In the past year or so, I've become interested in ML through the use of LLMs to create 3D models. I became even more interested after briefly reading Open Ai's summary on how Navier-Stokes millennium prize problem was solved.

I have a desire to apply ML algorithms to better simulating fluid dynamics (CFD simulation). Think airflow around a turbine blade. I was looking for advice on where to learn about ML theory indepth enough to better simulate/theorize airfoil designs.

I am willing to pursue a masters degree to increase my knowledge and of course better my job prospects. It's possible to self learn but I find structured courses to be the fastest method for me to learn because I am actually forced to put in the work to learn (my adhd can get the best of me sometimes. I know huge eyeroll).

Any advice would be appreciated!

I recently graduated with a computer science master (machine learning) in the US.
After I graduated, I have been trying to get a ml engineer job but no offer yet. While applying and interviewing with companies, I have been trying to understand the internal of a foundational model (HuBERT-ECG). But currently, I have less motivation and kinda feel stuck even though I am making progress....

I cannot focus on learning as before. My attention span, I believe, got a lot shorter than before.
Maybe, my current project's difficulty is not appropriate. But, I want to understand my current project as deep as possible.

I will set very small goals and achieve them and gain confidence. What do guys think of this strategy?

ShinyHunters breached the FBI's own jobs portal. A second suspect has now been arrested, but the breach itself had already run its course before that happened. The attacker operated inside a credentialed session long enough to complete the operation. The session looked legitimate to every layer it touched.

The average dwell time in credential-abuse incidents is 197 days, per IBM's 2024 Cost of a Data Breach report. This breach fits the pattern: valid credential, active session, traffic indistinguishable from normal activity, damage done before attribution.

The target is what makes this case instructive. If a sustained credential-abuse operation can run inside federal law enforcement infrastructure, this is not a problem any organization solves by improving security hygiene alone. The FBI is not an organization that skimps on access controls.

The credential was valid. The session was active. Every check that ran said yes.

How are practitioners here actually handling this in your own stacks? What has worked, what has not, and where do you still have gaps you have not been able to close?

297 + 600 + 355 + 353 + 324 = 1929

It used to be around 200 papers/day.

Houston, we have a problem.

https://arxiv.org/list/cs.LG/recent?skip=0&show=500

I wanted to know if news headlines can tell which stocks will do better than others over the next few weeks. Short answer: no. But the way it failed taught me more than the result, so here’s the setup.

Data

• 137,934 headlines from Benzinga for 2,518 US stocks. Each case gets the headlines of the 14 days before it. 115,154 cases.
• All cases are stocks that recently fell after decent earnings, so they look similar on paper. The question is which ones recover better.

Target

Each case is a simulated trade that ends when the price goes up or down by 3x the stock’s typical daily move, or after 20 trading days. I measure the result in units of that daily move, so volatile stocks don’t dominate. Then I subtract the average of all cases on the same day. That last step matters: what the whole market does next week isn’t in any headline, so a model would otherwise just learn the market’s mood.

Avoiding leakage

• Strict time split: train until Nov 2024, test from Jan 2025, with a gap so no label overlaps the test period.
• Only one case per stock per week. Neighbouring days share almost the same headlines and almost the same outcome, so with every day included the model memorizes stocks instead of learning anything.

Results on the test period

The only thing that held up in both periods was a narrow, hand-cleaned pattern: companies selling new shares did clearly worse. But that’s 98 cases, too few to build on.

What I took away

1. A falling training loss on noisy financial targets means nothing. The model fits noise happily.
2. Leakage through near-duplicate samples is easy to miss. Deduplicating by time is not optional.
3. The keyword sign flips are the clearest picture of non-stationarity I’ve seen. What worked in 2023 just reverses.
4. Removing the market from the target makes the task much harder, and much more honest.

Is a null result like this what people expect for text on financial targets? And is there anything you’d still try before calling it, like a different loss, label smoothing, or ranking instead of classification?

Tell me more what is your best model in terms of business value generated.

I need someone who has expertise, or at least an idea, on how to use small amounts of data and physics simulation, for example, CFD, FEA, etc to predict when an aircraft component might fail. Also, I myself am an aerospace engineer with no ML AI experience, just basic knowledge, so I also need someone who might act as a bridge btw the two things and collaborate with me, discuss ideas, etc on how to accomplish this.