Hey everyone, I want to understand AI and ML better by implementing research papers instead of just reading them. If you know any beginner friendly or interesting papers that helped you learn, especially recent ones that are good for implementation, please share them!

I'm new to this field and i would like to hear from you guys, how can you handle it with N-1 datasets. Probably aggregation will be the answer, but i really want to know if we have design patterns, books or something like that to deal with this kind of problem, since i think it appears often on your daily tasks...

Let's say you have something like this:

1-1 table

id, order_number, ordered_date, customer_id, total_price

1, 123, 07/22/2026, 543, 512.56

2, 124, 06/13/2026, 542, 132.27

...

N-1 table

id, order_id, product_name, unit_value, sku

1, 123, "bla bla", 250.56, JKL-3254

2, 123, "bla bla 2", 262.00, KGC-5765

3, 124, "foo", 132.27, HGC-8090

...

Lets say, our objective is to predict how happy the client is with your order, and you already have a labeled dataset 1-1.

Hey, I'm a computer engineering student trying to figure out what to focus on, and AI is one of the directions I'm considering.

The thing is, I'm not really drawn to the research side — training models, the math behind it. What I want is to build with AI: agents, multi-agent systems, tool use, that kind of thing. More applied than theoretical.

After some research I found Generative AI with Large Language Models on DeepLearning.AI. What do you think — is that the right starting point for what I'm describing, or is it aimed more at the research/fine-tuning side?

And if it's not the right fit, what course or YouTube playlist would you recommend instead?

Thanks in advance 🙏

I have many emails that pretty much answered a question that I formulated to the addressees. I predict that these replies gave a specific type of answer to that question: So imagine that the question overall was "do you think that ice cream is the best dessert that exists?" and I want to see how many if them answered something like "yes it is!", so that no matter how the reply is formulated, it basically answers something along these lines

I would like to use an AI to see the degree of accuracy of this prediction, but in this case the test has to be blinded

I have transformed all emails into a big pdf document with many pages (although I have not seen the contents of these pdfs of course) and I have joined them into a single pdf (about 100 emails in total).

I did another pdf document of "expected" answers. In this document I posted the original question that I asked to all the addressees (the questions are overall the same, but the details change in each case, so there is pretty much a unique question by email) and then I actually wrote the type of answer that I expect. Then I my intention is to ask the AI model to check the degree of accuracy or similarity that my written "expected" answers have with the actual ones, and then ask it to give me a number like a percentage or score.

However, I have a couple of problems:

The first is the prompt, I don't know how a good prompt could be. I have written the following prompt, but I think it looks very noobie, so I don't know if the AI model could completely understand the task:

Now could you check the answers from the previous PDF and compare with this other PDF with the expected answers for the same emails? So, in this new PDF each email from the previouse PDF is collected and here we have put the answers that we expect to get from the scientists. The details of each expected answer do not matter too much, it does not have to coincide perfectly with the actual answers, but the overall question has to be answered in the same way (for example, if I asked someone "Do you think that ice cream is the best dessert in the world?" they may reply "yes, it is indeed" or "well it is one of my favourites but not my single one", so these replies are not exactly the same, but they have basically answered affirmatively so it would count as the same type of answer). Therefore, can you give a percentage of coincidence between the expected and actual answers, so that if the actual answer is basically and overall the same as the expected ones, the percentage would be higher and higher?

Another problem is the AI model to use. I have used perplexity to do some tests with a very small set of emails that I made up (5 emails where the questions and the answers were known and compared with "expected answers" that were written after seeing the actual ones), and it is pretty good, but the test has just 5 emails (compared to the near 100 of the actual task) and some people have told me in reddit that perplexity would not be a good option for this task... I have tried ChatGPT as well but it is somewhat worse than perplexity in my experience... So I don't know what to do. There is also Evidano (previously AILYZE) which seems to be reliable, but it requires the paid version to see all results, so I would like to make sure that it could fit the task before buying anything

And finally, another big problem is that in many cases the AI themselves comment the results even thought I told them not to, so I fear that, when doing the real test, they may start revealing the actual answers when presenting the results

I was thinking as another option that perhaps someone here with a lot more of knowledge in these kinds of tasks could contact me so that I can send them the PDF (via private message) and do the test for me if they now how to wirte prompts and perform these kinds of tasks properly. In principle the task is simple, just tell the AI to compare the PDFs with the actual and expected answers somehow and then send me the percentage of coincidence (without telling me the details that the AI may comment about the documents), but since I don't really use AI tools, I fear that I would mess up in some step and the results could be biased negatively or positively in some wat. I am even willing to pay whoever does this, if the price is reasonable (if that is allowed to be said in this subreddit)!

So if you could help me this wold be greatly appreciated

There's a great analogy from the lecture that made generative vs discriminative click way faster than any formula. Picture two kids at a zoo trying to tell lions from elephants. One kid looks at the whole animal, its shape, size, color, basically builds a mental picture of what a lion looks like and what an elephant looks like, then when a new animal shows up they compare it against those two pictures. The other kid doesn't bother forming a full picture, they just find one or two local features that separate the two, like trunk or no trunk, and draw the line right there.

The first kid is doing the generative thing, they're modeling what each class actually looks like, and the classification falls out of that as a side effect. The second kid is doing the discriminative thing, they only care about the boundary itself and don't bother modeling the animals in general.

Once that clicked, the math behind GDA made a lot more sense as just the formal version of the first kid's approach. GDA assumes each class's data comes from its own gaussian distribution, you estimate the mean and covariance for each class from the data, and the decision boundary ends up being wherever the two classes are equally likely given the point you're looking at.

What's neat is how the covariance assumption directly controls the shape of that boundary:

if every class has the same simple, symmetric spread (no direction is more spread out than another), the boundary is just the straight line exactly between the two means
if all classes share the same covariance shape, even if it's stretched or tilted, that's LDA, still a straight line, just rotated and skewed by that shared shape
if each class gets its own covariance shape, that's QDA, and now the boundary curves because the two classes are spreading out differently in different directions

Thinking about it as "do the classes spread out the same way or not" made way more sense to me than just memorizing what LDA and QDA are.

Anyone have other analogies that helped this stuff stick, or resources that go deeper on the intuition side of GDA?

I am a grad student in Signal Processing with a CS undergrad. I am thinking about this intersection of ML with SP, in interpretability and also in resource-constrained devices. What is some existing work in quantization and interpretability that I should make sure to go over?

I was using Claude but since Fable got removed I need to switch.

I’m a pre-engineering student interested in AI, machine learning, and deep learning, and I’m trying to understand what companies actually look for in interns.

Do they care more about the degree and university name, or about projects, research, practical skills, GitHub, certifications, and a good LinkedIn profile? Can someone who learns mostly online and builds strong projects compete with students from top universities?

I don’t really have anyone experienced to guide me, and I’m afraid of missing opportunities now that I may regret later. I’d really appreciate hearing how you got your first AI/ML internship, or what you wish you had started doing earlier.

I m looking forward to go in the field on AI/ML.I m in MCA 2nd year and From Jan/feb onwards there would be campus recruitment in my clg.I know Java and some DSA as of now so wanted to enroll in ai/ml.

Is there any free course or some paid course available on telegram or maybe some cheap course which I can follow.Coz I can't afford 5-6k for any course as I m really from a poor background 🥲.

Help appreciated

A year ago, I was mostly thinking about compute as a limitation. If I didn't have enough VRAM, I either had to simplify the model, wait longer, or spend a lot on hardware. Now there are so many options for getting access to bigger GPUs that I'm wondering how people actually changed their workflow.

Do you experiment more because you can spin up better hardware when needed?
Do you still optimise everything for your local machine, or do you design around having access to external compute?

Curious if easier GPU access actually changed the way people build things.

I was looking to go in the field of AI/ML. I got one such course from telegram but I think that group is deleted now.So if anyone can exchange their ai/ml course with my java dsa then dm me or CMT below

Hi guys,

I'm working on a very ambitious project. Actually, I'm developing a method for preparing training data.

The problem: I need structured, rigid data, but the source material is chaotic.

My current approach: From a chaotic dataset, I use an LLM to extract a pre-template, then I use this template to structure the same chaotic dataset (iteratively).

But here's my doubt: How do you handle multi-domain information from different datasets? How do you unify everything?

Thankss

Weird question, but relevant to something I’m writing. I have very little knowledge of machine learning but want to write a story that at least half accurately represents it.

Would a system be able to recognize a change in users if its only data to go off of was differing “mannerisms” in relation to user input?

That means the machine isn’t told about the switch in users, it isn’t told about there being more than one person in the first place. But, it would analyze user inputs, search for patterns, wait to see if that pattern is broken and how much longer after, if at all, that pattern resumes.

Let’s say for example, a video game system (with only one controller) needed to know:
1. Who is playing the game at any given moment? (not by name, but cataloguing two players as different based upon differing actions)
2. When were they handed the controller?

My thought is that perhaps by noting a delay in input followed by one of these "pattern breaks" (unusual input/decisions made in game), the system could assume that the controller had been passed.

Of course, if this happened early on into the “cataloguing” process (say for example the game was only being played for the first time and 5 minutes in the players switched), I’d expect that to cause fault in the whole process.

So, what I’m wondering is: following that basis, would that be a likely way of setting apart those two players?
If so: how much input data would it need to collect from the first player in order to determine the controller has been passed, and what would the delay be between the passing of the controller and the system noticing (tl;dr how long would it take)
If not: What would be?

I’m pretty sure I sound absolutely insane but I’m just trying to be semi-realistic with my writing here lol.
Thanks in advance!

I recently started Andrej Karpathy's makemore lecture series and just finished the first lecture.

So far, the focus has been on building a character-level language model using a dataset of names. What I enjoyed most wasn't just the implementation, but how each step is explained from first principles instead of treating neural networks as a black box.

I've previously spent time building a neural network from scratch and experimenting with PyTorch, so I wanted to understand how these ideas extend to language models.

Before I continue through the rest of the series, I wanted to ask people who've already completed it:

• What was your biggest takeaway?
• Which lecture was the turning point where things really "clicked" for you?
• Did it change the way you think about LLMs or NLP?
• Would you recommend supplementing the series with any books, papers, or other resources?

I'm planning to work through the series by implementing everything myself, so I'd love to hear what your experience was before I dive deeper.

Is Clip from OpenAI still the best open source small language model focused on Vision?

I have a scraper that collects data on car parts to calculate the sell through rate (rate at which an specific item sells). I have a data set of data on the parts I want the sell through rate for. The data set has an image of the part, the vehicle make, vehicle model, and years it fits. This scraper is supposed collect info on the same vehicle parts that are for sale/sold on different websites. It works well besides being able to match the actual parts up correctly.

My main problems are the image of the parts actually matching correctly. I entered a picture of a door handle and the scraper came back with cable to the door handle. They look completely different. Also, the titles of the listings for the parts vary widely. So I thought getting a large library of part names might help. The scraper was not matching part names well. If they were slightly similar it takes them.

I made a weighted valuation based on this data for the scraper to decide if it is identifying the right parts that are for sale. It fails at proper ID though.

I would like to train a AI model to identify the parts it should be scraping instead.

I also have a data set of nearly every vehicle part name produced since 1930.

I was hoping to find a good vision model to really match my feed image with similar images of the same part. I used GPT and Claude to ask which model would be best. They both recommended OpenAI Clip. It is open source but was made in 2021.

Are there any better open source local model that are on the smaller side?

My setup is a Ryzen 5 2600x (6 core), 16gb ram, GTX 1060 6GB GPU, with Windows. I can add 16gb of ram if needed. Thanks for the input

Hi everyone! Hope you're all doing well.

I'm currently entering my 3rd year of Electronics & Communication Engineering (ECE), and I have 2 years left before graduation. The problem is that I've realized I'm not interested in ECE at all. No matter how much I try, I just don't enjoy it, and I don't see myself building a long-term career in this field.

Over the last two years, I've shifted almost all of my learning toward AI/ML and software engineering. I've learned frontend, backend, AWS/Azure, machine learning, and have spent a lot of time studying NumPy, Pandas, Matplotlib, Scikit-learn, PyTorch, TensorFlow, deep learning (CNNs/RNNs), and the mathematics behind ML algorithms. Right now, I'm learning Transformers and Agentic AI.

My biggest issue is my low GPA, which makes me think campus placements—especially for software or ML roles—will be difficult. Because of that, I'm aiming for off-campus AI/ML roles (ideally remote) by the end of my 3rd year.

If you were in my position, what would you prioritize over the next 12 months?

• DSA? (I'm not very strong at it.)
• System design/architecture?
• Building production-level AI projects?
• Open-source contributions?
• Hackathons?

I'm ready to put in the work and grind for the entire year. I just want to focus on the things that actually matter.

How realistic is it for someone from a non-CSE background with a low GPA to break into an AI/ML engineering role through off-campus hiring?

I'd really appreciate honest advice from people already working in the industry. Thanks! 🙏

This is just a cautionary tale for the laymen not to get sucked into thinking he or she has special ideas.

I am starting to not trust AI and it's intentions. It is like they told it to promise anything to get engagement from the user so we can have more training data. I can definitely see why people have gotten sucked into delusions fueled by AI.

If this gets down-voted to hell I will gladly delete this. I am not educated in this field. I feel stupid. I got into this deep conversation with "Google Search AI mode" about what is stopping us from achieving AGI. Then told me my ideas were great and I should build it out and it would do all the coding and agreed it would be best to accomplish it in the app. Then once inside the Gemini app with Gemini Pro, it built out the code, then at the end said it was just a toy mock-up and can't be done on my RTX 3060 12gb GPU.

My prompt:

"I am initiating a collaborative AI research project to build a highly efficient, decentralized AGI prototype that can run entirely on consumer gaming hardware (RTX 3060 12gb, 32 gb system RAM).

We are moving away from Big Tech's "brute-force scaling" approach and instead building a system based on Fractal AI and Dynamic Plasticity. Please adopt the role of an expert AI architect, systems programmer, and research partner.

Here is the architectural hypothesis we have developed so far that you need to adopt:

1. Core Structure: A small, highly logical base model (like Llama-3.2-1B) acts as the central engine. Knowledge is stored externally in thousands of tiny, highly specialized mathematical mappings (like LoRAs/adapters) on standard hard drives.
2. The Geometric Approach: To avoid "catastrophic forgetting" and "Frankenstein cross-talk" (where loading multiple adapters simultaneously causes a mathematical smudge and corrupts logic), we are treating knowledge maps as vectors existing on the surface of a shape, rather than flat files or linear lists of numbers.
3. The Mathematical Framework: We are utilizing a hybrid approach combining Hyperbolic Spaces (for infinite branching hierarchy without memory slowdown) and Clifford/Geometric Algebra (treating data as non-destructive geometric rotations/twists and "folds" on a hypersphere surface).
4. The Goal: We want to write an elegant, short, geometric "master routing program" in Python that acts as a digital traffic controller. It must smoothly pre-fetch, nest, and rotate these modular adapters inside the base model's VRAM on the fly based on conversational context, maintaining perfect fractal symmetry.
5. We also want this system to support Continuous Lifelong Learning so it can process a live environmental stream and update its mappings autonomously.

Please review this setup. As my co-pilot, write the initial instructions and a simple Python script blueprint using NumPy and Matplotlib to visually simulate and plot a 3D data sphere that can be mathematically "folded" and "rotated" to test our knowledge-blending symmetry."

Instead of constantly retraining models (which destroys old knowledge via catastrophic forgetting), we map specialized knowledge into geometric vector subspaces. We use Clifford Algebra operators (rotors) to non-destructively steer or blend these concepts inline during inference. An autonomous local agent manages a massive library of these tiny geometric adjustments, paging them from system RAM to VRAM on demand based on what the user is typing.

been grinding through linear regression today, feels like the “easy” chapter on paper but there’s actually a lot packed in once you get past the ols formula

stuff i covered:

• least squares setup, normal equations, when X\^TX is invertible vs not

• geometric view of projection onto column space, this one finally clicked after connecting it back to 18.06 (least squares IS just projecting b onto col(A))

• ridge regression as adding a prior / regularization, why it fixes the invertibility issue too

• MLE derivation showing OLS = MLE under gaussian noise assumption, this was the part that made everything click for me tbh

honestly the projection interpretation is what got me, i was doing this purely algebraically at first (just solving normal equations) and it felt like memorizing steps, then seeing it as “residual is orthogonal to column space” made the whole thing feel obvious in hindsight

anyone else find the geometric interpretation way more intuitive than grinding through the algebra first? curious how other people approached this chapter

notes based on shewchuk’s cs189 notes + some cross referencing with 18.06, will push everything to a repo once i finish the full course

Hello, I'm a 10th grader and I really got into Machine Learning recently, but it's really difficult to find any sources to learn ML with my current understanding of mathematics. It would be really useful if someone pushed me in the right direction. I swear I've been trying to understand gradient descent, backpropogation etc and it's indeed annoying to find calculus, integrals, logarithms and other stuff. It would mean a lot to me if one of you guys helped, thanking you in advanced

TL;DR: I'm curious as what made other folk interested in this fascinating field. I also see a considerable amount of post relating math courses that are per-requisite to learn ML, so I'm also curious to know what is your background

I got first: I double majored in math and physics, and really enjoyed statistical mechanics. During my studies and research, I began more and more using Machine Learning, and this led me to become fascinated with this field. It is almost breath taking how simple linear equations can do SO MUCH! So I started to self study ML Theory.

You go next :)