day 7 of self studying ML/CS, today's notes are on SGD.

went through the batch gradient descent algorithm, second order taylor expansion around a stationary point, then hessian eigendecomposition and why it lets you decouple the loss into independent 1D problems along each eigenvector direction. also covered how the signs of the eigenvalues tell you whether a critical point is a min, max, or saddle, the convergence condition for the learning rate, and where the condition number κ comes from (long thin error surface = forced to use tiny learning rate = slow).

last part just briefly touches on SGD, momentum, and Adam and how each one addresses a specific problem that shows up in the analysis (full gradient too expensive -> SGD, large κ causing zig zag -> momentum, one learning rate not fitting every direction -> Adam). didn't get into the actual computations for these three yet, that was more of a quick overview in class, so if anyone has good resources for going deeper into those I'd appreciate it

pages are attached below, still working through the intuition on some of this so if I got anything wrong feel free to correct me

Hi, I just graduated in Bachelor of Computer Science, and I am interested in both AI engineer and ML engineer, and I am also considering what to learn if I do Master in the future.

Currently, my plan is to study both AI engineer (RAG, LLM apis, etc) and machine learning. Besides that, I think want to do projects in both fields in order to find a job.

My concern is should I focus on both or just keep my focus on one field? I am open to learn both at this time, but I also think that focus on one should be better. And for Master, if I do, I should probably go for ML because it seems that AI engineer doesnt need a Master degree. But it also means that I should stick to research my whole life. Is this correct?

I appreciate any advice and guidance, especially if you were in the same place with me or you are in the fields. Thank you so much!

There's always one more course you could take. One more tutorial to watch. One more concept you probably should understand better.

How do you know when to stop preparing and actually try building something?

Curious whether most people here waited until they felt ready or just picked a project and figured out what they didn't know along the way.

We spent the last months consolidating seven separate sequence classifiers into one multi-head model, our apex model, so to speak, and since the weights are now public, I wanted to share what worked and what surprised us.

Setup: a shared mmBERT-small encoder with seven task heads, binary injection (BCE), document class (7-way), tool type (14-way), tool operation (6-way), tool data-flow tags (3× BCE, multi-label), intent routing (5-way), and threat type (7-way).

The part that needed care: our training rows only carry labels for a subset of tasks, so absent tasks are masked out of the loss entirely. We ended up writing a self-test that asserts absent-task gradients are exactly zero, which caught two subtle bugs, and I'd recommend it to anyone doing similar masking. About 5k synthetic/real multi-task rows help the heads co-train; the test sets stay 100 % real data.

Held-out results per head: injection F1 0.962, documents 0.980, tool type 0.957, tool operation 0.945, tool tags 0.958, routing 0.916, threat 0.952.

Quantization: both the unified model and the dedicated single-task variants ship quantized -edge builds (ONNX INT8 + INT4 embeddings, from 96 MB) with measured parity benchmarks in the repos, the worst head loses 0.012 against FP32.

Was it worth it vs. seven dedicated models? We released both variants, so you can judge for yourself, the dedicated models score marginally higher on most tasks, but the unified one does one encoder pass instead of up to seven.

Our weak spot: routing, at 0.916. The intent classes overlap semantically ("write code that analyzes my data" is that code or analytics?), and I suspect the ambiguity is genuinely in the data. If you have ideas beyond relabeling, let me know :)

Weights and per-head metrics: https://huggingface.co/patronus-studio

when a self-driving company records 1000 hours of driving footage, what format does it end up on disk?Is it all H.265? Or do people actually keep raw frames lying around? And during training do you decode from compressed every epoch or do you pre-decode everything once and eat the storage cost?

Hi everyone! 👋

I'm Milena. I am a software developer with 5 years of experience, and I've recently decided to seriously explore AI Engineering / Machine Learning Engineering.

I've already experimented a bit with stuff like LangChain, RAG, but I'm still figuring out what the roadmap for becoming an AI Engineer actually looks like. There are so many directions -LLMs, RAG, agents, MLOps, fine-tuning, ML, AI infrastructure - that I'd love to explore them with someone instead of learning alone.

I'm looking for a study buddy (or a small group of 2–3 people) who wants to:

• explore the AI Engineering roadmap together,
• discuss what technologies and concepts are worth learning,
• present each other's projects,
• share interesting resources,
• exchange ideas on a regular basis.

I can dedicate 5–10 hours per week, and I'm looking for someone with a similar level of commitment.

Hey everyone, I want to understand AI and ML better by implementing research papers instead of just reading them. If you know any beginner friendly or interesting papers that helped you learn, especially recent ones that are good for implementation, please share them!

Hey, I'm a computer engineering student trying to figure out what to focus on, and AI is one of the directions I'm considering.

The thing is, I'm not really drawn to the research side — training models, the math behind it. What I want is to build with AI: agents, multi-agent systems, tool use, that kind of thing. More applied than theoretical.

After some research I found Generative AI with Large Language Models on DeepLearning.AI. What do you think — is that the right starting point for what I'm describing, or is it aimed more at the research/fine-tuning side?

And if it's not the right fit, what course or YouTube playlist would you recommend instead?

Thanks in advance 🙏

Hi guys , I am in my 3rd year of CS and i am currently learning Backend development , as i have been quite interested in AI , i want to know what are things i need to learn for become a backend dev who can intregate AI and work with it , i know about things like RAG , Vectordb etc
but i dont know the exact path or things to follow from basic to a level that i can build AI powered projects

Think of masked image pretraining as a fill in the blank exercise. The model hides patches and learns to recover information about them, which shapes the features used later.

LingBot-Vision v2 changes which blanks matter. A teacher first estimates where image structure changes, and more of the student's masked tokens are selected around those boundaries. For depth and segmentation, one patch in the wrong place at an object edge can matter more than a small color error on a wall.

A boundary is not an object label. It could come from a depth jump, shadow, texture change, or sensor noise. The downstream head still has to decide what changed. If a simple head recovers cleaner shapes from frozen features, the pretraining probably helped. The pretraining loss alone cannot solve segmentation.

Hi everyone,

I’m Marvel, a computational neuroscientist at Cambridge building robotics infrastructure.

I’m trying to better understand where current robotics teams struggle most in production.

If you’re working on manipulation, humanoids, or industrial robotics, I’d love to hear:
What’s your biggest deployment bottleneck today?
What problem keeps coming up that existing tools don’t solve well?

I’m happy to share what I’m building if it’s relevant, but my main goal is to learn from teams actually deploying robots.

Thanks!

Quick post for anyone who likes to follow along. A few days ago I shared DeepEpoch — a no-code desktop app that trains a game-playing AI just by watching you play. You play a 2D game, it records your screen + key presses, and learns to play like you.

First off: thank you to the people who actually downloaded it and gave it a spin. Seeing real users try it means a lot as a solo dev.

Right now I'm focused on making the first-run experience smoother and adding a clearer training status, so you always know what's happening while a model trains.

If you've tried it, I'd love to hear what worked and what didn't. Link in the comments.

Hey everyone,
I'm working on beefing up my AI engineering portfolio and I want to build some proper end-to-end projects from scratch. I’m tired of tutorials that just end in a local Jupyter environment.
I'm looking for free websites or video tutorials that cover the entire pipeline from A to Z. I want to build something that shows the whole process—doing a bit of data scraping and all that shit, setting up and integrating the actual AI model, and most importantly, deploying the final product as a live web app or website that people can actually interact with.
Does anyone know of any good YouTube channels, free platforms, or open-source guides that walk through this full full-stack workflow? Free resources are highly preferred right now!
Thanks in advance!

Hello All,

I wanted to check how is the Snowflake , DBT , ADF with Cortex AI course Training at Raj Cloud Technologies.

plssssssss helppppppp

Hello friends

I just want to learn machine learning but I have doubts

Because my university degree is not related to this field and is in educational sciences

Now I don't know if I should learn machine learning or not? And can I even make money with it or not?

Thanks for your guidance

#machine_learning

I wanted to share a project I've been working on: the EHR Risk Framework.

It's a free, MIT-licensed toolkit for building leakage-safe, calibrated, and explainable clinical risk prediction models from electronic health records.

The Problem It Solves

Clinical AI research has three major problems:

• Data leakage – A 2025 study in JAMA Network Open found that 40.2% of published AI models had leakage, meaning results were artificially inflated
• Poor calibration – Models give overconfident or underconfident predictions
• Lack of explainability – Clinicians don't trust "black box" models

What It Does

• Built-in leakage audit – automatically detects data leakage
• Model training – XGBoost, LightGBM, logistic regression, random forest
• Probability calibration – Platt scaling, isotonic regression, Brier score, ECE
• SHAP explainability – via UI and API
• One-command reproducibility – Docker workflow

Why It Matters

• MIT License – free for everyone
• Peer-reviewed validation – CRuSE-Heart published in Springer (2026)
• Aligns with federal priorities – 2025 SAFER Guides require AI transparency

Links

• Docs: https://ehr.larucare.com/
• GitHub: https://github.com/ranasl62/ehr-risk-framework

Would love to hear your feedback. If you work with EHR data, what features would make this more useful for your research?

I'm a CS student building my ML portfolio. I'd appreciate honest feedback on this Sales Forecasting project trained on roseman sales data on Kaggle. What would make this project stronger for entry-level ML roles? Link is given above for review.

https://github.com/abdullahnaeem151015-lgtm/Future-Sales-Forecasting-using-Machine-Learning

If some expert can give me an honest feedback on this project and point out the future improvements it will be helpful for my future project.

Today's notes cover logistic regression — starting from how it connects to LDA (same functional form, just trained discriminatively instead of generatively), then the sigmoid function and its properties, softmax for multi-class, and the MLE derivation for the loss function.

Second half covers evaluating classifiers: confusion matrix, ROC curves, and AUC. The spam email example (95% accuracy while catching zero spam) is a good reminder of why accuracy alone can be misleading.

I'm planning to keep doing this for every CS189 topic throughout the summer. Hopefully they'll be useful to anyone learning classical machine learning as well.

Feedback is always appreciated.

Hi everyone,

I’m 33 and based in Switzerland, working as a DevOps engineer with around 7 years of experience. My background is mostly in CI/CD, cloud infrastructure, Kubernetes, monitoring/observability, and automation – the usual DevOps toolkit. I have a degree in Computer Engineering, so I’m comfortable with programming and core CS concepts.

Over the last year I’ve become increasingly interested in AI/ML, especially MLOps and AI engineering. I’d like to transition my career in that direction (MLOps/platform/infra for ML systems, or AI/LLM engineering on the infra side).

To make this transition more “official” on my CV, I’m seriously considering a part‑time, fully online master’s degree in AI / data science / ML, ideally from a European or Swiss university, so that I can keep working while I study. My main goal with the master is:
• To have a recognized credential (MSc) that helps recruiters and companies take the shift seriously.
• To get structured coverage of ML/AI fundamentals, not to become a pure data scientist, but to understand the ML lifecycle well enough to do solid MLOps/ML platform work.

My questions for the community:
1. Has anyone here moved from DevOps to MLOps/ML platform roles around this age (early/mid‑30s)? How realistic is it, and what did your timeline look like?
2. From the hiring side, how much does an online master actually help versus strong projects + hands‑on MLOps skills?

For example, degrees like:
• Online/part‑time MSc in AI or Data Science from European universities (distance‑learning, 90–120 ECTS).
• Swiss or EU distance‑learning AI masters (UniDistance, Distance University/Idiap, IU, GoVersity, etc.).
3. If you were in my position (33, Computer Engineering degree, 7 years DevOps, Switzerland), where would you invest first?
• A serious online master for the credential and structured learning.
• Or several focused courses/bootcamps + self‑driven projects (MLOps, LLMOps, cloud AI, etc.) and skip the degree?

I’m already comfortable with Python for scripting and infra work, and I’m starting to read more ML/LLM papers and play with small projects, but I want to be strategic with time and money. Any honest experiences, advice, or “if I were you I’d do X/Y” perspectives from people already working in MLOps/AI (especially in Europe/remote roles) would be super helpful.[reddit]
Thanks in advance!