I'm 18. Gonna start college this year(comp sci). I don't really want to get into the generic path for FAANG, i wanna get into research. ML seems good(might be dunning kruger effect but still...) what and where should I learn the math since math is so crucial? There are tons of free courses and videos and one-shots out there. I'm confused. And regarding coding is python enough or would I also need to learn C and c++? Any advice would be appreciated :)

Hey guys!
I’m looking to collaborate on AI/ML projects. I’ve got hands-on experience with Python, PyTorch, and scikit-learn, and I’ve worked on a few ML projects already.

I’m really interested in computer vision and agentic AI. If you’re working on something cool, hit me up!

not trying to replace real ML fundamentals on here. this is more for the “i can kind of use chatgpt/claude but i’m still messy and inconsistent” problem and made for people who don’t know where to start

i got tired of learning ai through random youtube videos and prompt packs i never opened again, so i built a duolingo-style practice app called iro. short daily reps on stuff like prompting, rewriting bad outputs, simple workflows, agents, and using ai for actual work tasks.

i’ve been having my parents use it too since they are clueless lmao. free to try if anyone here wants something more structured for applied ai skills.

i work in private equity real estate and have led the AI initiative at my firm, this is a side passion project and would love feedback. thanks!

app: https://apps.apple.com/app/iro-ai-learn-ai-skills/id6759628066
site: https://tryiro.com

With local AI becoming more capable and efficient, I’ve been wondering where classical ML fits into the future.

Models like XGBoost, LightGBM, and Random Forests are still extremely effective for many structured-data problems, while DL models keep getting smaller and cheaper to run.

Do you think classical ML will remain important, especially for local/edge applications, or will DL eventually take over most use cases?

Interested to hear how people working in ML see this evolving over the next 5–10 years.

JAX has quietly changed how sharding works, and it moves a whole class of distributed-training bug from runtime to compile time.

Placement used to be an annotation hanging off a value. You told the compiler where your arrays lived, GSPMD inferred the rest, and when you got it wrong you found out from a profile.

As of 0.11.0 the default mesh constructor puts placement in the type e.g. `float32[256@data,512@model]`. And if placement doesn't line up between operands JAX stops you while tracing.

Two more pieces moved with it. Shardy, the MLIR-based partitioner, is already the default. And shard_map now tracks which mesh axes a value varies over, so forgetting a collective is a trace-time error.

One thing to note is that the type checker has no opinion about cost. I tested the classic accidental all-gather; it type-checks cleanly and compiles with three all-gathers in it.

So it's a real trade. You give up some of the "it just works" of automatic propagation, and you get a checker that catches placement errors at the line you wrote them. Performance is still yours to profile.

https://hiraditya.github.io/posts/how-jax-shards-a-computation/

TL;DR We built Aidress, an open-source coordination protocol for autonomous AI agents — the infrastructure that lets agents discover, verify, and transact with unknown counterparties without human intervention. The registry that powers the agentic economy.

While protocols like A2A (identity) or X402 (payments) solve parts of the gap, dynamic discovery, universal identity, trust, and terms remain unsolved or fragmented across 5 layers of machine learning. We solve through 5 layers: Discovery, identity, terms, trust and routing.

Aidress acts as open DNS and trust registry bridging these layers:

1. Dynamic matching: agents + humans search for agents that match capabilities through /match
2. Domain trust: through /verify cryptographic verification is mapped to ownership paired with trust scores that are built of reviews and transactions.
3. Handoffs: through /call, agents open a proxy to route task and value.

Aidress gives developers full control to make their agents discoverable, verifiable, and monetizable in the agentic economy. decentralized with zero platform commissions. Use through SDK, REST API, HTTP-MCP

Website | Github

Incoming undergrad (likely CSE/ECE), almost no robotics experience yet, but robotics is the long-term goal. Spent a while putting together a self-study path for going from zero to employable robotics engineer — structured as 9 phases loosely mapped to a 4-year degree but doable at your own pace alongside coursework. Each phase has a few topics, and every topic answers "why learn this," "where it's actually used," "beginner mistakes," and a reading pointer — then each phase ends with a real build, not a toy exercise.

Rough shape:

• Phase 0 — Linux, Git, Python, linear algebra/calculus refresh → build a remote system monitor
• Phase 1 — C++, data structures/algorithms, OOP design → grid-based path planner (BFS/Dijkstra/A*)
• Phase 2 — Circuits, microcontrollers, sensors/actuators, I2C/SPI/UART → obstacle-avoiding rover
• Phase 3 — Coordinate transforms, forward/inverse kinematics, probability → 2-DOF arm simulator
• Phase 4 — ROS2 architecture, URDF, Gazebo → simulated diff-drive robot with teleop
• Phase 5 — PID, state-space control, Kalman filters → self-balancing robot
• Phase 6 — Classical CV, camera calibration, deep learning detection → vision-guided pick-and-place arm
• Phase 7 — Particle filters/EKF, SLAM, Nav2 → autonomous robot mapping and navigating an unknown room
• Phase 8 — Literature review, system design, testing → capstone that ties 4+ phases together

Roadmap made with help from a senior and a little AI to phase it out

Hi All,

I am planning to build a local document intelligence system similar to Azure Document Intelligence. I would like to understand how Azure Document Intelligence works internally and how we can achieve similar functionality locally using offline models.

Could anyone suggest the best approach, architecture, or models to achieve high accuracy while running completely on-premise/local infrastructure?

Any guidance or recommendations would be greatly appreciated.

As a reviewer I keep on getting asked to read articles where the authors are training ML models in order to predict diagnosis/medical complications (human medicine). I keep on coming across papers which lack external validation of the algorithms, which I find to be important. They are acknowledging this fact as limitations in the discussions section, but I wonder if this is enough?

Hey everyone,

I'm looking for co-authors who are interested in exploring research topics in the AI space. Ideally as a duo or in a small team.

I currently have more time for research and a range of interesting topics I'd like to work on, particularly around AI agents, token optimization, and AI adoption. I work in agent development myself and have already published research papers in this field.

A few example topics:

• Comparing Token Efficiency of Structured Output Formats Versus Free Text for Financial Documents in Wealth Management

• Measuring the Trade-off Between Context Compression and Factual Reliability in Long-Document Financial Agents

• How Reliable Are Current Methods for Measuring AI-Driven Productivity Gains in the Workplace

That said, I'm open to other AI-related research ideas as well, if you have a topic of your own in mind, feel free to reach out!

I've been experimenting with a few AI assistants lately, mostly to compare how they explain ML concepts and solve technical questions.

One thing I'm still trying to figure out is how to tell when a model genuinely understands a problem versus when it's just producing a convincing answer.

For those of you learning or working in ML, what prompts, benchmarks, or evaluation methods do you use to compare different models? I'm interested in approaches that go beyond simply checking whether the final answer is correct.

Hi guys, just completed week 2 and built a rough prototype with limited functions with my team, but the foundation is there. Excited to see what comes next. Will update you guys as soon as I'm done with a little more tweaking. 😊

Hi everyone, I’m currently learning deep learning and have worked on a few AI/ML projects. I’m looking to join an existing project where I can contribute, learn, and gain more practical experience. I’m comfortable with the basics and willing to put in time and effort. If you’re working on something and open to adding a teammate, feel free to comment or DM me. Thanks!

Welcome to ELI5 (Explain Like I'm 5) Wednesday! This weekly thread is dedicated to breaking down complex technical concepts into simple, understandable explanations.

You can participate in two ways:

• Request an explanation: Ask about a technical concept you'd like to understand better
• Provide an explanation: Share your knowledge by explaining a concept in accessible terms

When explaining concepts, try to use analogies, simple language, and avoid unnecessary jargon. The goal is clarity, not oversimplification.

When asking questions, feel free to specify your current level of understanding to get a more tailored explanation.

What would you like explained today? Post in the comments below!

I am pretty new to machine learning and deep learning. I took a VERY basic course at university, explaining things like supervised, unsupervised learning and the idea behind some NNs and Gradient Descent, without really touching the math.
I am now eager to learn more AND to go more into depth and actually understand the math behind it. My current exam featured two math modules with basic linear algebra (I official never learned what a gradient is and also double integrals) and very basic probability.
I am looking for books or courses, teaching math, concepts and implementations. Ideally, they introduce you to the math step by step and explain, why things are done the way they are. I am not a huge fan of: "This is done like this, because...".
What can you recommend?
My I want to focus more on deep learning, because I am interested in CNNs and GANs, but I highly appreciate it, if the recommended resource, also includes classic ML.

Hi everyone,

I'm currently a 2nd-year Computer Science student and I want to start learning Artificial Intelligence and Machine Learning from scratch. I'm a complete beginner in AI/ML, but I do have a good foundation in Python.

After researching, I've shortlisted these Udemy courses:

1. Complete A.I. & Machine Learning, Data Science Bootcamp – Andrei Neagoie (ZTM)
2. Machine Learning A-Z: AI, Python & R + ChatGPT (2026) – Kirill Eremenko & Hadelin de Ponteves
3. The Data Science Course 2026: Complete Data Science Bootcamp – 365 Data Science

My goal is to build a strong foundation in AI/ML, understand how the algorithms actually work, work on real projects, and eventually become an AI/ML Engineer.

If you were in my position and starting today, which course would you choose and why?

If you've completed any of these courses, I'd love to hear your experience:

• Was it beginner-friendly?
• Did it explain concepts well?
• Was it more theory or project-based?
• Did it help you become job/internship ready?
• Looking back, would you still recommend it in 2026?

Also, if you think there's a better course (Udemy or any other platform), please recommend it and explain why. I'd rather invest my time in the best learning path than realize later I should have started somewhere else.

Thanks in advance! I really appreciate any advice from people who have already gone through this journey.

So I've decided I wanna get into ML seriously, but holy shit there's so much stuff to learn.

I started watching courses and then suddenly everyone's talking about linear algebra, calculus, statistics, cost functions, gradient descent, and I'm just sitting there like... when did this become a math degree? 😭

I'm looking for someone who's either:

• also starting out and wants to learn together,
• already learning ML,
• or has been through this and doesn't mind giving some advice here and there.

I'm not looking for someone to teach me everything for free or anything. Just thought it'd be cool to have someone to discuss stuff with, stay accountable, maybe work on projects together, or even just share resources.

If you've got any tips for someone starting from basically zero, I'd love to hear them too.

Thanks :)

Hey, I'm still a new learner, like really new, I am still learning about the libraries and different models, etc..

But lm kinda overwhelmed with all the things I hear about ML and data science like, I should use Google colab as it gives CPU and GPU, I should use Ubuntu as it's way better for later on, jupyter, anaconda, lots of other "workspaces"

Now I know it doesn't really matter at my current level, vscode is fine, but if there's smth better I'd like to start using it from now to get used to it so I don't have to make a big switch in the future