r/MLQuestions

I'm working on a signature verification project to detect fraudulent signatures on cheques. Here is how my pipeline looks so far:

​1. Extraction (Done)

I'm using a fine-tuned YOLOv8 model (from Tech4Humans) to detect and crop out the signatures from the cheque images. It’s working pretty great at isolating the signature area.

​2. Preprocessing (The tricky part)

Since cheques come from all kinds of different scanners and phone cameras, the backgrounds and lighting are all over the place. To clean them up before feeding them to the model, I'm doing:

​Ink isolation: Removing noise near the boundaries by thresholding pixels based on background brightness.

​Line removal: Scanning for long horizontal bars (like the signature lines on the cheque), wiping them out, and filling the gaps based on the surrounding texture.

​Contrast stretching: Using percentile contrast stretching to fix dark backgrounds caused by bad phone/scanner scans.

​Smoothing: Applying a bilateral filter to smooth out background noise while keeping the signature lines sharp.

​CLAHE: Using Contrast Limited Adaptive Histogram Equalization to boost the contrast of the ink against tough backgrounds.

​Padding: Adding final padding so the aspect ratio doesn't get warped.

3. The Model

Once preprocessed, I’m feeding the images into EfficientNet-B3 to train it to spot the differences between genuine and forged signatures.

The problem is my validation accuracy is not improving beyond 55-57,

what else can I do to improve this?

Is the preprocessing enough is should I improve it more, because I'm still getting some horizontal line like the signature line, and some text in some cases

Guys I am trying to learn ML and also wanted to be an ML engineer so should I study this book or suggest me any other books

Hi everyone, I'm in high school (I failed twice, embarrassing I know but I had various problems) and I would like to be a research scientist in the field of deep learning, I decided to do mathematics instead of taking the standard path with computer science at university,

so at the moment, in the summer of the fourth year of high school (in Italy we have 5 years of high school) I'm reading books on rigorous mathematics (real analysis, proof-based linear algebra) (struggling a lot), and since I hate the classic machine learning courses found online (I prefer books in general),

I was wondering what was the most rigorous way to approach this field (maybe after I have tackled multivariable calculus), I know that most of the knowledge comes from papers but, a general book would be handy, is "deep learning" by Goodfellow a valid choice or is it now out of date?

I'm not interested in DL libraries at all, and I'd like a source as formal as possible. In fact, I'll probably only use numpy and cupy (or even pure CUDA) until I start my PhD. (In general, I have good experience with programming and neural networks; I even created transformers with only numpy \[a sort of tensor micrograd\], but without fully understanding what was going on.).

Do you have any other general advice? Does this kind of atypical path make sense?

Right now I am working on YouTube Chatbot, where a user can paste the url of the video and ask questions based on that. I have followed classic RAG approach. The design looks like this :

Initial design :

(query, url) → YouTube Transcript API → Translate to English (Gemini 3.5 Flash) → Chunk → Vector store (Chroma) → Similarity search → Augment context with query → LLM → Output

Upgraded design :

(query, url) → YouTube Transcript API → Chunk raw transcript → Translate to English (Gemini 3.5 Flash) asynchronously each chunk → Vector store (Chroma) → Similarity search → Augment context with query → LLM → Output

I have some intermediate steps also like if the video id is already present in vector store I will directly point to the vector store and retrieve relevant context.

There is Langsmith integration.

My main doubt here is this :

1. I used free gemini-3.5-flash model and it limited me to only 5 requests per minute, the problem is a particular video was 1 hr long it took approx 126 seconds to translate it using this model

2. I upgraded it to Tier 1 and I have changed the translation step to asynchronous, i.e, the chunks will get translated in parallel and I noticed the latency drop to 15 seconds.

I am thinking of mentioning this project in my resume, will I face any backlash because I upgraded the model?? (I think basically the model will take the same time, it is the asynchronous logic which helped in bringing the latency down to 15 sec, to make these calls happen I had to increase my Tier and get those extra calls per minute).

So last night at around 11pm I noticed I felt weak and tired and so I went to bed and slept horribly. My stomach was aching all night, I was nauseous and felt cold and off. I wake up at 7:40 to my dog barking and I immediately feel horrible. I check my temperature and it's 102 and so I go back to bed. I wake up again at 10 am and take Tylenol and my temperature before Tylenol is 101. It got down to 98.5 after Tylenol and went back up 4 hours later and got to 101. Then I take another nap for 30 minutes and wake up and take my temperature and it's 100 and I take Tylenol and then chill. My temperature goes back down to 98. My symptoms are: stomach ache, diarrhea, fever below 102, headache, weakness, body aches, fatigue.

Hello Everyone!!, I just completed a BIG project I have been working for a month and i want your opinion about it.

It's a SpaceX Launch Predictor & Cost Optimizer (A full end-to-end ML system that predicts the probability of a SpaceX Falcon 9 booster landing successfully, enriches launch data with real weather conditions, and exposes the results through an interactive Streamlit web application with a business ROI calculator.)

It Includes Data Pipeline, Advanced Machine Learning Algorithms (with Hyperparameter tuning), Explainability AI (SHAP), MLOps (AWS S3, Docker) and Business Value (ROI Calculator = Financial Results).

FUN FACT: For this project i used my own Evaluation Metric library (standardizes supervised and unsupervised model diagnostics into a single, consistent API), that is also Verified and Published in PYPI Community.

Project Info: https://github.com/Alkiviadisss/SpaceX

Can you tell me some neural networking projects for beginner level person

I recently built a human written digit predictor.

Now I want to start a new project can you guys give some suggestions

I have an interview for the Tesla Optimus team as an intern specifically doing machine learning and reinforcement learning stuff. I've not been told what the interview will be about, only that I will be programming in Python. I've been preparing for it through a number of different ways:

• Implementing various algorithms (MLP, various optimizers and regularization methods, CNN, forward pass, backward pass, etc.) using just Numpy and PyTorch from scratch with a heavy emphasis on vectorizing everything
• Going over the math for all the major ML architectures (MLP, CNN, RNN, Transformer, etc)
• Going over the math for all popular RL algorithms (DQN, PPO, SAC)
• Making sure I know everything on my resume

Is there anything else that I should be doing or looking at? I haven't really done any LeetCode as I assumed it wouldn't focus on my LeetCode skills, should I brush up on that as well? Any tips would be greatly appreciated!

I asked this question to google, and of course the google AI told me that google was the best...

I want to take a video game character and recolor/redesign some of the armor sets in different poses.

I was wondering if there is a proven technique that works for knowledge distillation in the context of time series forecasting.

I have been trying alignment in the latent space with the Frobenius norm of Gram matrices as alignment loss, but results are not that impressive so far.

Any recommendations? Thanks!

Hi everyone,

I'm currently a medical student with a long-term goal of pursuing a PhD in a top lab working on machine learning applications in translational medicine and healthcare.

Right now, I know the basics of ML. I've completed a few Coursera courses, implemented some personal projects, and have basic Python experience. However, I'm struggling to figure out how to take the next step. I want to build the kind of skills and portfolio that would make me competitive for world-class research labs.

For those of you working in ML for healthcare, computational biology, or related fields, what would you recommend focusing on? Should I prioritize open source contributions, reproducing papers, Kaggle, research internships, reading papers, or something else?

Also, if anyone here works in this space, I'd love to connect, learn from your experience, and see if there might be opportunities to collaborate on research or open source projects.

Thanks in advance!

Hey everyone!

I am a secondary student working on an AI-driven, dynamic learning platform for software engineers upskilling to AI/ML roles. 

It has two main features, adapted to this specific task. 

1. Through diagnostics (such as novel problems, asking the user to explain concepts, and other techniques that you might see in a job interview for example) it develops a detailed learner model of the depth of user’s understanding on a topic-by-topic basis, visualised in a colour-coded graph so that the user can aggressively attack their weaknesses and develop proper skill and understanding. 
2. World-class content is already publicly available online. Instead of investing 100s of hours into experts authoring new content, the platform curates tried-and-tested content made by the very best in the field to form a curriculum. My impression is that AI/ML roles require ever-changing skills, and this architecture would allow the curriculum to be able to adapt extremely quickly, with comparable or sometimes even higher quality content than what would be available with static curriculums.

 

I thought that this wonderful community of developers would be a great place to validate the idea, so for those who:

1. Have transitioned from software engineering to AI/ML
2. Are currently transitioning
3. Are planning to switch roles
4. Or if you’ve used upskilling services whatsoever

Would this help you?

Any feedback would be greatly appreciated; Thanks in advance.

(P.S. I am planning to make it subscription based, something around €20 / month. )

I'm and AI focused ICE (Informatics and Communication Engineering) Bachelor’s student I've been seeing alot of ai/ml/data science roles even internships requiring master's degree or higher.

What roles in the AI field usually hire Bachelor’s degree holders?

Hey all.

I recently started working on a project to improve machine-translated webnovels via style transfer. The basic idea is to take the clunky translated prose and rewrite it to something that reads like it was written by a professional author, while remaining as faithful as possible to the original text.

The source material is mostly amateur/MTL output full of direct sentence structure translations carried over from Chinese, awkward honorifics, over-translated idioms, that kind of thing. The goal isn't retranslation from the source but a cleanup of the English output.

The tricky part is I have no clean data pair for supervised approaches.

I've been looking at a few directions:

• Fine-tuning on target-style prose — collect high-quality English novels, fine-tune a small LLM to rewrite in that register.
• Just use a local LLM — run a local LLM and provide it with guidelines on what to rewrite and leave the same. No fine-tuning or anything needed, just hoping the transformer can handle it.

A few things I'm stuck on:

1. Is the faithfulness/fluency tradeoff actually manageable at the sentence level, or do I need paragraph-level context or more to preserve narrative coherence?
2. How do people handle domain-specific terms like

terminology

1. and catchphrase-type things that need to survive the rewrite unchanged? Hard constraints during decoding, or just hope the model learns to leave them alone?

Happy to hear about similar projects, relevant papers I might have missed, or just general lessons from working in this space. Thanks.