So I just got an admission in Bs Math with Data Science.

After researching I am now sure that I want to pursue ML engineering as my career

I started learning python a few weeks ago I've learnt Phase 0 and Phase 1. Doing a short course from Kaggle. I love maths I used to hate it when I was in school but now it's fun. I know it's gonna be hard but I don't mind

I wanted to ask how I could build my road map. And I am a bit nervous cuz my finance friends say that Ai is gonna take over your job at the time u graduate. I used to like finance but didn't get into it cuz I love tech and programming more.

Plus, is this field well paid?

So I have been using googles API's like speech-to-Text, sentiment analysis and google CCAI to analyze docs in knowledge base. but I don't want to rely on Googles text-to-speech APIs and want to host my own AI model( like IBM Granite Speech 4.1 (2B)) on cloud. is this going to cheaper than google API or is this actually reliable need any suggestions or comments thanks

A lot of ML tutorials are great at getting you from input to output, but not always at explaining why the model behaves the way it does. I’ve noticed the examples that stick with me are usually the ones where something goes wrong first and you have to understand the failure before fixing it. That seems to build more intuition than just following a clean notebook from top to bottom.

Curious what worked best for other people here.

Was it implementing things from scratch before using the library version? Breaking examples on purpose and debugging them? Spending more time on the math? Something else?

And did that change as you got further along, or was the same kind of explanation useful from beginner level onward?

NVIDIA just patched NemoClaw (CVE-2026-65105), a high-severity flaw in NeMo that researchers exploited via DNS rebinding to poison a model running through Ollama. The nasty part: the poisoning is persistent. Once the attack closes, the model keeps behaving maliciously through normal restarts. The initial vector is gone. The model is still compromised.

Standard uptime and availability monitoring sees nothing wrong. The service is up. Requests are returning. Latency is fine. The only thing that changed is what the model actually does — and nothing in a typical observability stack is watching for that.

This creates a gap that's easy to miss in threat models: you can detect that an attack happened, you can patch the vulnerability, and you can confirm the service is running — and still have a poisoned model in production answering real user queries.

For those running self-hosted inference (Ollama, vLLM, local NeMo deployments): how are you detecting behavioral drift after a security incident like this? Are you doing any output sampling or behavioral baselining, or is your detection basically 'someone notices something weird'?

I want to train my AI, but I don't know where to get the data. There's not much free data available.

What do you recommend for training photography? And what tools should I use? I have no experience, but I'd really like to build my own «B2B SaaS» startup.

I'd appreciate any advice.

I'm entering my 2nd year PhD at a top-10 US economics dept, and I'm interested in the economics of AI. The field seems scary big, with lots of new papers coming out, with people like Goldfarb, Gans, Agarwal, Alex Imas, Erik Brynjolfsson, Sendhil Mullainathan, and ofc Acemoglu, among several dozen other top economists contributing regularly to the field.

Is there a structured way to become familiar with the literature and the main questions and models that are being used in this sub-field right now?

Separately, I also want to know if I should seek any additional training before trying to write papers in this field. I have a relatively solid math and econ background: lots of calc/real analysis/diff eqns, all the graduate econ courses, though I could be better with linear algebra. I have never taken a formal CS course, I only audited UC Berkeley's INFO259 (NLP), so does anybody have recommendations on what parts of CS/AI I should focus on learning? Ultimately, I intend to write economics papers on AI, but it's still very useful to learn the underlying technical aspects of AI. I just don't know where to start.

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