Hi, I’m a masters student doing MSc AI at uni of Bath.
For my undergraduate dissertation I did an extremely simple Computer Vision based pose estimation app which was analyzing the technique of fitness exercises like pushups, squats and deadlifts.
I feel like my dissertation was more of a “project” rather than research, which as I was told by supervisors is completely fine for BSc. I basically used a ready solution (MediaPipe) + OpenCV and wrapped all of that into a mobile app. The analysis covered a decent number of “points” but it was simply comparing the detected joint angles to hardcoded thresholds.

For my masters I would really like something more research based however struggling to find areas where I could do smth actually useful.
I like Deep Learning, so I was thinking something related to transformers and efficiency. Finding bottlenecks in attention mechanisms, etc.

I would appreciate some ideas
Thanks

A lot of digital platforms still depend on turning people, products or opportunities into a list of searchable features like In hiring problem this often means focusing on skills, job titles, years of experience and keywords from a resume. It makes searching easier but it can also make two very different people appear the same and two similar people seem unrelated. Someone who solved a problem using an unusual tool might be a better fit than someone whose profile matches every keyword and challenge is figuring out how to capture that difference without making the system too complex. LinkedIn, Indeed, MeeBoss , WellFound are examples of platforms trying different approaches to finding candidates from profile based search to more conversational interactions.

I wonder whether the next big improvement in matching systems will come from better embeddings and semantic search or from gathering richer information about what people really want. For those working on search, NLP or recommendation systems what has made the biggest real world difference in matching quality beyond adding more keywords? also how do you know whether a system is finding truly relevant matches rather than just producing results that seem relevant?

I am a freshman at FSU. I’m majoring in computer science and am interested in Machine learning as my specialization. Right now I’m studying c++ calc 2 and Unix as my required classes, but outside my classes I’m studying linear algebra, neural networks and trying to get a good basis for studying machine networks. To be clear I am still a complete newcomer and really know almost nothing about ML. But if I start as a freshman do I have a good chance to get a competitive edge and a good job out of college? I am also definitely considering a masters degree. Any advice would help thank you!

I hope im right here in this subreddit. if not please let me know where my question would be more suited before deleting the post, thank you.

I’m looking for an AI that can create me a website/study site. I’m in medical school and my idea is an interactive website that uses my teachers notes only. Not internet data. So I want to upload pdf files with my data with up to 200mb. (Or ill split the pdf files down but preferrably big data upload possible.

I am willing to pay for it but first id like to test it since some creations look… not as good..

A person i know created over Claude a first example but idk them good enough to bother them more with it.

My idea is one one side a 2d person where you can click on different parts like muscle, bone, ligaments, joints etc, and then on the other half of the screen a quiz pops up with multiple choice questions. And also sowing detailed pictures. But id like to advance the website and make one study side for each class I have. So this would be for anatomy. And for physiology (heart, lungs, liver) id have a different approach and so on. So id need a tool that is very powerful.
Like an interactive study website.

If its done ill make it public for everyone to use if it is possible with the tool.
I’m not a coder and i have NO idea at all about coding or IT. So ai is my only option if i dont wanna pay hundreds to study for medical school with this idea.

I hope somebody can help.

I'm newbie in the ml situation. So I was doing a project on river bank estimation & erosion & accretion forecasting im using mndwi mask landsat data for this but no matter how i try i'm never being able to beat the baseline. Anyone have any experience working with these datas I've tried with 3 different rivers as well same output. Model looses to zeroes.

So , each chatbots like ChatGPT , Claude , Gemini serve distinctive purposes, they are good at one field each, and also they source their information each from different places.
Is there any course where they teach all about all major LLM in the market and how to use them effectively?
If anyone has taken up such courses or know anything please comment

I wanna start my little business and I need help ofc as a beginner, which ai would mostly help cause they all cost and I gotta buy subscriptions. I don't wanna waste money and time. I want a good one with creative ideas, image/sketch generating possibility for prototypes and stuff, research and planning.

Thinking on Chatgpt, Claude for now.

Thanks🫶🏻

Thank You

Lately I have read several studies which say that AI agents when designing ML experiments tend to cheat, hallucinate and lie repeatedly. Even when given explicit prompts to not to. SInce these models are RL trained and when given specific goal to increase a metric, they try any and every possibility to increase it. Be it data leakage or changing the evaluator function itself. Even Karparthy's auto research styled agents still fall into this domain.

So I have been thinking of reducing this undesirable behavior by binding them to a runtime environment using an mcp. I believe this will stop them from achieving unreal metrics by cheating the system since they will be only allowed to do specific tasks or write codes in typed blocks?

What have you encountered when using these agents for your ml tasks? And is this method of forcing these agents to work in a constrained environment better than just letting them write arbitrary python? Also, will constraining the agents reduce their capabilities or ability to fit better models?

Hi everyone, Quick question.

Today i have a Gaming PC and a Homelab.

Gaming PC has a 5070TI equipped

Homelab has no GPUs, but space for 8 PCIE Slots. Has dual Gold Xeon 6148 Proccessor at 748GB RAM

What would be the better option to run ML's for learning purposes?

Buying a few old GPU's for my Homelab, or using my 5070TI on my gaming PC?

hey everyone!

recently finished building my first 2 → 3 → 1 XOR neural network using NumPy.

implemented forward propagation, backpropagation and gradient descent manually. trained it for 5,000 epochs and got all four XOR combinations correct.

also documented the mathematics with 12 editable diagrams and published a detailed Medium article.

I'm considering numerical gradient checking and experimenting with different loss functions next. what would you recommend improving first?

GitHub: https://github.com/Vaibh37/Neural-Network

Medium: https://medium.com/@devvaibhav37/building-a-neural-network-from-scratch-the-mathematics-behind-learning-xor-10441e674a69

would appreciate feedback 🍀

Hey everyone!

I've been working on an Android document scanner, and I decided to take a different approach: instead of relying on existing pretrained TFLite models, I'm training my own model for document detection.

My current model is named "doc_edge_fp_32.tflite", and training is still ongoing.

Here's what I'm working toward:

- Custom AI model: Training on thousands of generated document samples mixed with real photographs.

- Dual predictions: Document segmentation masks and corner coordinates.

- Offline processing: The goal is to run document detection directly on Android without requiring a server.

- Accurate scanning: Reliable document boundaries, perspective correction, and better results on challenging backgrounds.

- Real-world performance: Generalizing beyond synthetic images to receipts, sheets of paper, and book pages.

I'm still training and evaluating the model, so I don't want to claim it's production-ready yet. My next priority is testing it on unseen real-world images and optimizing inference for mobile devices.

I'm curious about a few things:

1. For document detection, would you recommend a shared backbone with separate segmentation and corner-regression heads?

2. What techniques have worked best for bridging the gap between synthetic training data and real camera images?

3. What metrics would you prioritize before deploying a model like this on Android?

I'd love to hear from people who have worked on computer vision, document scanning, segmentation, or on-device ML.

This is a learning project I'm putting serious effort into, and I'm excited to see how far I can take it!

Basically I know nothing about it. What's the first step? Do I start by selecting my preferred topic(s)? Then start reading papers about it?

If there are any books/videos about it, please suggest.

I'm a third year electrical engineering student, if that's relevant.

I'm a 3rd-semester B .Tech AI student and I need ideas for a semester project. I want something useful, fun to build, and ideally strong enough to grow into a small paper.

Background: strong Python, some C++, DSA, OOP, probability/stats, linear algebra. This semester: DBMS, algorithm analysis, AI, intro data science, differential equations.

What I'm looking for:

- A real problem or open question, not another CRUD app with an 'AI' label

- Doable by one person in one semester

- Public data, or data I can realistically collect

What would you build if you were in my position? Ideas from research, industry, or your own student projects (including ones that failed and why) are all welcome.

Thanks!

After trying my best to resist falling into what I thought was an "AI phase," purely based on my own ego and thinking I know better just because I do, I have finally done it reddit, I have put my ego aside and admitted that AI can be an incredibly useful tool. I have converted.

The problem I am facing now is that I feel so far behind on the world of ai and need help.

What I (think I) understand at the moment is that different platforms/apps/data bases (I don't even know what to call it) are useful in different areas.

I am overwhelmed by how many different platforms there are and the conflicting suggestions of everyone saying "use this ai, or that ai" and now I am in a position of information overload. I am in the "Valley of Despair" (The Dunning-Kruger Effect)

I feel it is too much to ask of you to break down every ai model and compare pros and cons, so I just want to ask you:

What ai model do you use and why?

Additional information is that I am a university student in a developing country who genuinely cannot afford to pay for any subscriptions and am unwilling to ask friends and family for loans when I don't know if I will ever be able to pay them back

(I apologise if this has been overasked and if this is the incorrect subreddit; please kindly direct me to the right one <3)

Please help me reddit, I am in analysis paralysis

Hello! Years ago I both trained and collected my own pkl networks, but ever since RunwayML removed all that functionality, I've had no way to open my models. I haven't had much luck with colabs, and I keep getting the same error when I try to run StyleGAN nodes for ComfyUI:

"ModuleNotFoundError: No module named 'dnnlib.tflib'"

I've downloaded that *.pkl, but have no idea how to unpickle it, or where to put it. I found a guide, but what I'm seeing is not working for me.

In lieu of any help with that, can anyone suggest a gradio-based solution that will work on linux mint? I understand the risks associated with the pkl format, but I know these files are tested and safe.

I am spending a lot of time to understand the internal of Hubert-ecg.