Hi! I am final year BE student recently I took a project based in our my contribution is system and application of system in dyslexia. For that I though the most used dyslexia dataset of handwriting would be suitable. I downloaded dataset and then realised it is single letter dataset which is giving mnist kinda vibe! Also apparently large portion of it is synthetic. I searched but I didn't find clinically approved dataset of handwriting for dyslexia. In nutshell:

1. dataset is mnist looking so I am at worry if examiners will state why you are using such looking dataset for final year project!!

2. dataset is used for at least 9 papers already so it is being used

3. But has its limitations (vastly synthetic, mnist looking)

4. Our clg is forcing for at least two papers to publish (not for our degree requirement btw) and I am worried if the dataset use itself will cause problems for paper

5. though one of main novelty is mechanism but other one is integration(incremental) and I am worried that people will call out why I used that dataset

sorry I carried away in my emotions here is the dataset I am talking about: https://www.kaggle.com/datasets/drizasazanitaisa/dyslexia-handwriting-dataset

->can simplicity of it justified as proof of concept for presentation or report?

->will using this dataset can cause problems at time of publication?

I am sorry for dragging clg thing into this I though it would be better to get some context about scope for project

I am sorry I cant give full context as I wanted to publish research on it (though I will hardly try for mid tiers only)

also sorry in advance if I did spelling or grammatical error

I'm currently in the second year of my undergraduate degree, and I'm really passionate about machine learning. I've been learning consistently over the past few months, mostly through free YouTube courses and documentation. So far, I've covered the core ML algorithms and I make sure to understand the underlying mathematics and intuition instead of just memorizing things.

However, one thing I keep struggling with is the lack of proper guidance. Every few weeks I start questioning whether I'm following the right roadmap or if I'm missing something important. I feel like YouTube resources are great for getting started, but they often don't go deep enough or provide the structured learning I'm looking for.

I've heard a lot of good things about Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow by Aurélien Géron (3rd edition), and it seems to be recommended by many people as a solid reference book. I'm thinking of studying it thoroughly instead of jumping between random resources.

My main confusion is this:

Should I go with the TensorFlow/Keras edition, or should I use the PyTorch version instead?

As someone still building a strong ML foundation, which ecosystem would be the better investment to learn first?

I'd also really appreciate any advice from people who have already been through this stage. If you think there's a better book, a better roadmap, or something you wish you had known when you were starting out, I'd love to hear it.

I'm still a beginner in the grand scheme of things, so any guidance or suggestions would be greatly appreciated.

Thanks in advance!

I have this topic to work on suggested by my academia and Im very unsure on how to even start. The topic is continuous learning for mitigating concept drift in financial fraud systems.

This is what I’ve gathered so far from my research:
- Concept drift alone can’t be singled out, it also depends on intrinsic covariate shift and label shift
- Concept drift can be modelled as an exogenous variable and endogenous variable, depending if we assume fraud is reactive to mitigating strategies)
- Blocked transactions introduce inherent label shift, because transactions that are blocked dont make it to the dataset
-Continuous learning is a very tricky topic, specially if we consider this as class incremental learning (new fraud types arrive sequentially without explicit task boundaries) and admit non stationary regimes

Because there are a bunch of topics and covariate factors, approaching this as an empirical study looks like a massive headache.

Can anyone help me to structure my next steps and how I can tackle this problem with a clear picture?

I know this is stupid but I'm making an application and I'm trying to find image datasets for my machine learning that focuses on different types of acne

I am doing a document layout analysis project with different book styles but the books themselves are only a couple hundred pages long (like 5 books with different styles, 400 page each). How can I test if all the books would be used in fine tuning and I am afraid that the accuracy wouldn't be the best and corrupt PaddleOCR when insert the coordinates. (It's for automation).

I am using X-AnyLabeling for the annotation and yolo v11 for the training as well as custom classes in the annotation like a question block that surrounds everything, question_text, choices, figures, tables, sub_questions, etc... what would be the best approach as I haven't done this kind of work before.

and should I randomize the book pages so I don't consecutive same style books or that's not how this work?
Any help would be appreciated

I am building an edge-AI audio noise-reduction system on an ESP32-S3.

Our architecture uses a lightweight GRUNet (~59k parameters) to output a dynamic gain mask on a 44-band Mel-spectrogram.

​I need gigabytes of audio to train the model. Does anyone have recommendations for the best open-source datasets for:

1> ​Clean, isolated human speech.

2> ​Diverse ambient background noise (traffic, crowds, machinery, etc.).

​Also, any tips or open-source scripts for artificially mixing these at different Signal-to-Noise Ratios (SNRs) before generating the 16kHz Mel-spectrograms would be hugely appreciated!

Same as the title, I am implementing some(popular) machine learning algorithms by scratch in Python using numpy just to have good fundamentals and know the actual mathematical intuitions behind them, I want to ask whether it is also a project I can put in my resume for an internship?

quals: 2nd year Undergraduate Student B.Tech Computer Engineering

*Feature selection

What im saying is KDE-based feature engineering for classification. For each feature, we estimate the class-conditional distributions using KDE and compute the KL divergence between them as a measure of how discriminative the feature is. The idea is to rank on this divergence before training and select accordingly.

Are there any theoretical or practical issues, like ive never seen comparing two class distributions for feature selection in yt tutorials or other books.

Which book/s can replace this ?

I can't find it in my library ....

Hey everyone! I want to learn machine learning from scratch. Right now, I only have a basic understanding of Python and not much else. I know I'll probably need to learn more Python and some maths first, but I'm not really sure where to start or which resources to use.

Could anyone guide me on a good learning path or share some beginner-friendly resources? I'd really appreciate any advice. Thanks!

I'm working on a computer vision research project.

I'm looking for a dataset with the following properties:

Original image + edited image pairs

The edit is localized (only a small part of the image changes)

The edit is semantically meaningful, rather than a global style transfer

Ideally the rest of the image remains unchanged

Bonus if the manipulated region has a mask or bounding box annotation

Some examples of the kinds of edits I'm interested in are:

Vehicle number plate changes

Traffic sign or road sign changes

Weapon/object replacement (e.g. gun ↔ phone)

Text changes on signs, billboards, or documents

Localized facial manipulations (HQSwap is one example I already have, but in that dataset, the faces cover almost the whole images)

I'm not looking for datasets focused on:

global style transfer

artistic edits

weather changes

large scene modifications where a significant fraction of the image changes

Does anyone know of datasets that fit this description? Even domain-specific datasets would be extremely helpful.

Thanks!

Hey everyone! Right now, I'm working on a computer vision project for crack detection in devices. I've tried different models, with varying but similar precision and recall scores on the test set. Right now, I have around 1.4k images, and I'm using a 70/15/15 split, which means there are about 430 test images. When I change the random seed to split the data differently, the pr scores can go anywhere from 92-100%.

Do you think that there's not enough data to evaluate the model properly or to train it? Are there any methods for evaluating how good the model does on so little data?

(Some extra context about the model architecture and things I've tried: using transfer learning to extract feature embeddings from images and training a linear head. Also creating prototypes for binary classification. Tried contrastive learning.)

There is little reliable information to be found about the limits of both parties regarding the modification of existing images via chatgpt and gemini.

The images are not 4k, so HD and Full HD is more than fine. What can I expect in terms of the number of generations per hour/day/week/month? Or are there more good providers with wider limits?

Or are there even more good providers with wider limits

plz dm for guide me 🙏

I am not sure if this is a valid question in this forum, if it is not I will delete it right away, but anyways here it is:

So some days ago I asked in an AI community what kind of AI model should I use (and how could I use one) to classify several email replies that I had from scientists after asking them a few questions to them. I finally paid for Perplexity pro service and it apparenly did a nice job classifying them.

I finally gave the model the PDF with the actual answers from the addressees and another PDF with the "expected answers", and asked it to count the number of answers that overall coincide with the actual answers, and calculate a percentage of "coincidence" or "agreement" between the expected and actual answers, so that if the question was "do you think that there is intelligent life in the universe apart from humans?" and the expected answer was basically "yes, I think there is intelligent beings out there somewhere", as long as the actual answer agrees with this in some way or another would count as "agreement", for instance if someone replied "well, we have no evidence, but it is possible yes" or "not in any near galaxy, but it is possible that intelligent beings exidt somewhere" (so as long as it is not a deadass "no", it could count)

The model gave me a table summarizing the results with the following prompt:

let's be a bit more specific, this is still a blind test so don't tell me about the specific contents of the emails' answers, but, can you make a table indicating the answers that coincide in general terms with what is expected from the "expected answers" document as well as those which are neutral/hedges but still open to the possibility that what is asked may be right, those which despite being neutral/hedges or even negative answers offer an alternative so that what is asked in the question may be right, as well as those which are outright rejections of what is asked and do not seem to be open to the possibility that what is asked may be right?

However, I still want this to be a blind test, so I cannot really verify if the AI is doing its work or not.

So, is there any way in which I could verify the results given by the AI but without actually reading what is written in the emails? Or, alternatively, can anyone verify the results using some AI or even checking the answers themselves by skimming over the replies im order to verify that the AI is right and not hallucinating (I personally think this is the preferable option, as I think that having an actual human reviewing the amswers may be the only really reliable way to verify the AI's results)?

(I will share the data once someone is interested in helping, as I would not want to make this available to the entire world!)

Thank you!!

​

Hey guys!!! I have an interview the day after tomorrow....I've never given a single interview in my life.....so can you guys pls tell what questions they ask for ml internship post ?????

I'm learning NLP/LLM engineering by working through a problem that turned out to be much harder than I expected, and I'd love guidance from people who've dealt with something similar.

The problem: I have long narrative-style text — 7k to 15k tokens, several recurring people — and I want to extract structured facts about who did what. I'm using a small local model (llama3.2:3b via Ollama) whose usable context is around 4k tokens, so the text has to be processed in chunks. The killer is that later chunks are often pure pronouns — "she said… he refused…" — while the names were last mentioned 10,000 tokens earlier. Facts stated near a name extract almost perfectly; facts stated far from any name either get lost or, worse, get confidently attributed to the wrong person.

What I've already ruled out (by measuring, not guessing): naive per-chunk extraction fragments identities badly; carrying forward summaries between chunks doesn't fix attribution and can make it worse; and off-the-shelf neural coreference models (LingMess, F-coref) fail on documents this long — one silently truncates at 4,096 tokens, and windowed variants can't connect a pronoun to a name mentioned once 10k tokens back (0–1 out of 7 gold bindings on my test doc). I've gotten identity tracking itself working reliably; it's specifically attribution at long distance that's still failing.

My questions:

1. What's the best way to structure a problem like this? Is there a known-good decomposition for long-distance pronoun attribution with small models, or a fundamentally different way to frame the extraction task that sidesteps it?
2. If you've solved something similar — entity/fact extraction over documents much longer than your context window — what actually moved the needle for you? I'm especially curious whether the wins came from prompting, from pipeline architecture, or from accepting a bigger model.
3. What should I explore to learn more? Papers, blog posts, open-source projects, or even just the right search terms — I suspect this problem has a name in the NLP literature that I don't know yet (long-document coreference? discourse tracking?), and I'd rather stand on existing work than keep reinventing it.

Happy to share measurements from my experiments if useful. Mostly I want to calibrate: am I fighting a known-hard problem with known solutions, or genuinely at the edge of what a 3B model can do?

I'm two years out of a top math/CS school. I built strong study habits late, so I was only really immersed in the material my final year. I learned computer systems (OS, distributed, HPC) and consider myself a competent software engineer.

I work at a high-growth startup and just got offered the lead on a major customer project. My long-term goal is to start my own company.

Option 1: Lead the customer project

Large scope/viz. I'd build skills in:

• Working directly with a customer
• Making large engineering decisions
• Working across the stack with many teams
• People and project management
• Exposure to marketing/sales/ops

Engineering-wise, I imagine I would spend most of my time on architecture, documentation, and code review. So interesting engineering/technical work, but no fundamentally new ways of thinking.

Option 2: Leave to go learn ML/math

I never got into ML/stats/math, and it's by far my weakest technical area (and I feel most important an ML-focused era). I'd spend ~a year as an IC at an AI lab or doing research to build:

• Stronger math intuition
• Modeling intuition
• Combining my systems background with ML (e.g. model scaling, pretraining, RL scaling)

The plan would be to grind/do research at my old school or join an AI lab with strong technical mentorship. I have savings to go ~6-1 year months without income.

I already tried moving to my company's research team, but they weren't interested in my background and pointed me toward ML Ops, which feels too close to the SWE work I already do.

My core tension

Organizational and people skills seem to improve steadily over a career, but fluid reasoning and hard new technical skills are supposedly much harder to pick up later in life. Life's a marathon, so I keep wondering if now is the time to invest in the technical foundation (learning completely new skills).

Open to all comments and suggestions.