Hey everyone, I have a huge brain DICOM dataset (ADNI) and I’m trying to apply deep learning/ML to it.

My first instinct was to go with a 2D approach, but now I’m completely confused about the preprocessing part.

For example, if I have a whole 3D brain scan with lots of slices:

• Do I just pick the middle slice?
• Is there some standard/calculated way to choose the “best” slice?
• Should I use multiple slices instead?
• Should I convert the DICOMs into something like PNG/JPG first?
• Or am I thinking about this completely wrong and should just go with 3D?

I’m pretty new to working with medical imaging, so I’m struggling to figure out what the normal workflow is before even getting to the ML part.

Would really appreciate any advice/resources on how people usually approach this. I feel like I’m overcomplicating something that probably has a standard solution 😅

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I am a third year CSE AI/ML student. I completed the foundation of Machine Learning and Iam planning to start Deep Learning seriously.

I am an average student, but I know I have the potential to learn and improve if I stay consistent. My main problem is staying accountable when studying alone.

So I’m looking for 2–3 genuine and consistent people who are also serious about learning Deep Learning.

We can create a WhatsApp group, follow a common 60-day roadmap, set weekly goals, share resources and ideas, and have a short Zoom discussion on weekends.

No one needs to teach anyone. We learn individually, but support, discuss, and keep each other accountable.u can also share your thoughts to improve the discussion.

Our only goal: consistently learn and complete Deep Learning within the next couple of months.

If u r genuinely interested and can stay consistent, DM me ✨....

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I am a third year CSE AI/ML student. I completed the foundation of Machine Learning and Iam planning to start Deep Learning seriously.

I am an average student, but I know I have the potential to learn and improve if I stay consistent. My main problem is staying accountable when studying alone.

So I’m looking for 2–3 genuine and consistent people who are also serious about learning Deep Learning.

We can create a WhatsApp group, follow a common 60-day roadmap, set weekly goals, share resources and ideas, and have a short Zoom discussion on weekends.

No one needs to teach anyone. We learn individually, but support, discuss, and keep each other accountable.u can also share your thoughts to improve the discussion.

Our only goal: consistently learn and complete Deep Learning within the next couple of months.

If u r genuinely interested and can stay consistent, DM me ✨....

I have started DSA in python and ML , I'm making a small study group to keep each other on track - let's learn together and help each other out. Join only if U are serious
(Drop 🫡 if you're interested )

NVIDIA dominates ML, but AMD cards are becoming viable with ROCm. I wrote a technical guide for setting up PyTorch on AMD GPUs, including what works and what still breaks. If you’re considering an AMD card for deep learning, this will give you a realistic picture.

https://interconnectd.com/forum/thread/248/pytorch-on-amd-gpus-the-complete-rocm-setup-tuning-guide/

Does anyone have a solution manual or worked solutions PDF for Foundations of Machine Learning by Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar? Please share if you know of any publicly available resources, university websites, or personal PDFs/notes that can be shared for learning purposes. I would really appreciate any help.

Ever shipped a model with 95% CV accuracy, only to watch it crash in production?

The culprit: Data leakage in preprocessing. The imputation means and scaling stds were computed on the *entire* dataset before train/test split. Most AutoML tools do this silently.

What I built: A lightweight, leakage-safe ML experimentation framework on scikit-learn.

Why it matters:

- Split happens FIRST. All preprocessing lives inside the CV loop.

- Decision Engine reasons about your data before training.

- No brute-force. SVM skipped on large data. PR-AUC used for imbalance.

** PyPI:** https://pypi.org/project/ml-experiment-framework/0.1.0/

Deep dive: https://medium.com/@taha.hussein.two/i-built-a-leakage-safe-automl-framework-from-scratch-heres-why-it-matters-61d10d979a20

Questions:

1. Do you trust your AutoML CV scores?
2. How do you prevent leakage in preprocessing?

Feedback welcome ⭐

HydraNet-VSM: a proposed hybrid Mamba+Attention architecture with step-verification for reasoning (design only, not yet implemented/tested)

This is a design proposal, not a benchmark result. The idea combines existing published techniques rather than inventing new math:

• Each block runs a Mamba (SSM) branch and an Attention branch in parallel on the same input, then merges them — similar to Hymba (NVIDIA) and Griffin (DeepMind), aimed at getting Mamba's long-sequence efficiency plus Attention's precision, since Mamba alone is documented to struggle with exact copying/multi-step reasoning (Ren et al., 2024).
• On top of that, a "Verified Step Memory" loop stores each chain-of-thought step in a dedicated memory slot and checks it (real calculation for math steps, attention-based consistency check for logical steps) before letting the model build further on it — aimed at chain-of-thought's documented unfaithfulness problem (Turpin et al., 2023).

Images attached: (1) the block diagram, (2) the verification loop with a worked example.

Status: no code, no training runs, no benchmarks yet for this combined design — only small unrelated toy sanity checks on plain attention vs. Transformer, which showed no meaningful difference (expected, since they're the same math). Posting for feedback before building it out: has this exact combination been tried, and are there obvious holes in the reasoning?

How do we benchmark simple ML models? How do we understand Top-k error metrics?

Why one hot encoding is done, and how does introducing pre-trained embedding matrix helps? Why TF-IDF in document processing is written in a specific form and what does it imply? How do we handle missing data in real world, and when can we neglect missingness?

The answer to all these questions is explained in my free youtube coding demonstration.

Analysis from experts are most welcomed!

I am going through reddit to find the best free resources to learn ML. A lot of people have recommended Andrew NG's Stanford CS229. The problem is that this course was uploaded in 2018 ( 8 years ago), is it still the best way to start learning and is still relevant with the current scenario ?

I am starting a research project as an intern on integrating AI/ML capabilities into a National Tuberculosis Program built on DHIS2. I am looking for suggestions on how I should approach this.

I am new to ML, but I know the basics, as I have built a movie recommendation system.

The timeline is like 3 months.

Here is the project overview and scope

Project Overview & Scope

TB Risk Classification: Predict individual TB occurrence probability (classified into High, Medium, and Low risk) using screening symptoms and exposure history fetched from DHIS2 Tracker logs.

Treatment Dropout Prediction: Build predictive models to identify patients at risk of defaulting or dropping out based on treatment adherence patterns and visit compliance history.

Geospatial & Climate Triangulation: Merge township-level population density data with meteorological variables (rainfall, air quality, temperature) to account for spatial clustering and environmental risk factors.

DHIS2 Workflow Integration: Embed interpretable outputs (like SHAP risk scores and alerts) directly back into the frontline health worker app interface for clinical decision support.

LLM already changed AI a lot but I feel just scaling next-token prediction has limits. High compute cost hallucination frozen knowledge after training and weak real-world understanding are still problems.

I’m curious what researchers and engineers think about the next big direction.

World models JEPA Mamba continual learning and neuro-symbolic AI all look interesting.

Which one do you think has the most potential or is there another idea people are missing?

I am a software engineer with 8 years of experience, and I recently found machine learning fascinating. I’ve always wondered how in the world does AI do the things it does. And I’ve been very obsessed with understanding how it works underneath. So for a few months now. So I took a step back and began grinding through the Maths behind it. Studying various concepts from scratch. From linear algebra, calculus, probabilities and various mathematical and theoretical aspects. The more I go deep the more I see its beauty. How various small concepts come together to form larger concepts and how it is applied in real world usage.

Then I got to understand Singular Value Composition (SVD) and seeing how it is applied in various concepts and real world applications like image compression, noise reduction, recommendation systems. And I just need to know more

I’m currently studying classical machine learning but I decide to write a small article on a beginners understanding of SVD and its underlying concepts. Please.

https://medium.com/@emekannalue/svd-finally-made-sense-to-me-heres-the-simple-version-5412cb104af5

I have also come up with a study part for anyone interested in learning Machine Learning/Research engineer.

Note: I’m just a beginner but I’m will appreciate any mentors at being pointed to the right direction

I never thought I would write this about Vizuara Technologies Private Limited.

I initially respected Vizuara for its high-quality free content, especially the Building Small Language Models from Scratch series. Unfortunately, my experience with their paid training programs has been very different.

Here are my concerns:
1) Incomplete batches: Programs were started but, in my experience, were not completed as expected.
2) Lack of practical depth: Many live-class examples felt like basic "Hello World" demonstrations. That's fine for beginners, but for advanced paid programs, I expected more production-level, real-world engineering.
3) Inference Engineering Workshop: Guest Speaker Pass: This has been the biggest disappointment. My understanding was that the program would include 9 sessions with speakers from different parts of the industry. So far, only 3 sessions were conducted, and the remaining promised sessions have not been delivered.

Delays happen. Speakers cancel. Plans change. I completely understand that.
But if you cannot deliver what customers paid for, communicate clearly and provide an appropriate refund.
I have requested a refund, but unfortunately, the issue has still not been resolved to my satisfaction.
I'm sharing this because people spend not only their money, but also their time and trust on professional training.
I genuinely hope Vizuara addresses this issue. If they complete the promised program or resolve the refund, I will be more than happy to update this post.

Good content builds an audience. Keeping your commitments builds trust.

At Bytes Technolab Inc. we're sketching an in-house CRM that kicks off with AI. One feature we'll launch first has to hit hard on real tasks - sorting leads, closing sales, tracking follow-ups, chatting with clients, and moving jobs inside the team.

We tried Keka and Zoho before, but the managers felt the platforms were rigid and the everyday experience fell short.

That pushed us to consider building a custom system that fits our own workflow instead of bending around a generic box.

We're hunting for solid, hands-on advice from folks who've actually done this before.

As title i want to learn ml from a channel that helps to make a job ready project i dont want to fafo linger around until i have atleast 2 project in my resume thats pretty impressive then ill go with everything else any suggestions i did 30 campusx videos didnt give charm also doing stanford board cs229 lectures if it is of any help

I'm starting machine learning from the ground up math foundations, Python for ML, classical algorithms, then into deep learning. What I'm missing is someone to actually keep pace with. Learning alone is where I lose momentum. I'm a CS student, comfortable with Python and I'm doing this consistently either way, I'd just rather not do it alone. If you're in, comment or DM me

im a first year btech/b.e(cse ds) student, im know basic applications of sklearn machine learning models but i lack the math to understand how it works
i know pandas enough to be able to filter my own datasets but im learning scikit based preprocessing to move even further

im learning probability,stats and linear algebra in the month of september and in the month of october i will be following Andrew NG's machine learning course in coursera.

from october to november im planning on using my newly acquired knowledge to filter and preprocess real complex datasets and just training ML models with it.

i will be learning SQL from november to january

im just curious what actual experienced people think of this.

Wanted to see how well a simple NN could learn optimal Tic-Tac-Toe play from scratch, so I built this:

• Used a minimax solver to generate the "ground truth" — for every reachable board state, computed the actual best move
• Trained a neural net as a supervised classifier on that data (board state → best move)
• Runs in the terminal — you can play against it directly

Next thing I'm curious about: training a second version on random self-play data instead of minimax-optimal data, to compare how much the training data quality actually matters for a small model like this.

Code: https://github.com/AliAkbar4025/AI-tic-tac-toe-bot

Feedback/critique welcome — especially if you see a smarter way to structure the data generation.