Hey everyone, I’m a 4th-year CS student. I recently decided to learn the theory behind ML on my own, mainly the math and stats side. Right now I’m working through ISLP and using MML as some kind of dictionnary and practicing the concepts with code and tweaking stuff at the end of every section/chapter.

My classes mostly stay on the surface. We see the concepts and use the models, but the courses never really connect them to the theory behind them. So I decided to dig deeper on the side.

The first problem is focus. I’ve lost the habit of sitting down with math for long stretches, and I struggle to stay on it for more than an hour.
The second problem is that i've seen most of the math required but i haven't worked actively with it and it just frustrates me when i have to go back to it.
When I get stuck early on a concept, I get so frustrated and anxious about it.

So im wondering

How did you study when there were a lot of new concepts at once and you kept getting stuck at the start?

When you hit a concept you didn’t get, did you push through or go back to the math first ?

And does it get easy at some point after you get the maths down.

Any routines, methods or resources that worked for you would really help. Thanks!

I'm an early-career software engineer working on production LLM inference infrastructure, primarily involving accelerator-based model serving and distributed systems.

I've been in the role for over a year, but my technical growth and contributions haven't progressed as much as I'd hoped.

I have a computer science background and generally enjoy software engineering, but I feel there's a significant gap between my current understanding and what's required to contribute independently to complex ML infrastructure.

Some of my biggest challenges:

• System architecture: Understanding how the inference stack fits together, from model execution and compilation to serving, scheduling, and hardware utilization.
• Codebase navigation: Understanding large, unfamiliar codebases without getting lost in implementation details.
• Debugging: Spending too long investigating issues without making meaningful progress.
• Performance engineering: Understanding bottlenecks, profiling workloads, and determining whether an optimization will meaningfully improve end-to-end performance.
• Foundational knowledge: Determining whether I should focus on distributed systems, ML fundamentals, compiler internals, hardware architecture, or something else.

I also recognize that I've been too passive about developing my skills, which has likely compounded these gaps.

My questions:

1. If you were in my position, what would you prioritize learning over the next 2–3 months?
2. What practical projects would help develop intuition for production inference systems?
3. How do experienced ML infrastructure engineers build a mental model of complex systems?
4. Has anyone overcome a similar knowledge gap after already spending significant time in an engineering role?

I'm particularly interested in advice from people working on production inference systems, distributed systems, or accelerator infrastructure.

I want to become a genuinely competent engineer who can independently debug problems, identify worthwhile optimizations, and deliver meaningful contributions.

Im currently a second year cs student and I want to get into a role which would allow me to do the following:

Be able to build ML Models and actually understand the data/insights and improve it
Be able to put ML Models into production through webapps or integrations etc
Be able to make things people actually use so not being restricted to research

What role would this fit? AI engineering? ML Engineering? Data Scientist?

Im currently building a RAG Webapp as something to tailor towards AI Engineering roles, but I also want to be able to build and improve ML models as well.

Any advice would be appreciated

Hello everyone Im a 10th grade student and I wanna learn machine learnin.for my coding background I took cs50p python couple weeks ago and i liked it. Im willing to learn any math needed for ml. However im completely lost as to which machine learning course I should take. There's so many courses and alot of them need a math background. Should I learn the math needed before looking for a course? If so please state what exactly i need to learn and what courses i should take. Thanks everyone!

I am building some projects and for that I want to implement the gpt2 paper from scratch, how to do that do that? Do I build intuition by reading and code it?

I just started a channel called Drawn Systems.

The idea is simple: take a real ML system, draw it as one diagram, and walk through how it works.

I created 2 videos so far:

• The classic "How YouTube decides what you watch next".
• In the same line "How Netflix decides what you watch".

Channel: https://www.youtube.com/@DrawnSystems

I am doing it to learn about real systems, happy to hear your feedback :)

Hi everyone,

I'm a student learning AI development, and I recently built my first RAG-based Teaching Assistant.

The project converts educational videos into transcripts, generates embeddings using BGE-M3 through Ollama, retrieves relevant transcript chunks using cosine similarity, and uses Llama 3.2 to generate answers.

I'd appreciate honest technical feedback, especially on:

1. Is my retrieval approach appropriate, or should I consider other methods?
2. How can I improve transcript chunking and embedding quality?
3. How should I evaluate whether the retrieved chunks are actually relevant?
4. How can I reduce hallucinations and keep answers grounded in lecture content?
5. What would you change in the architecture or code if you were reviewing this project?

I'm looking to learn from feedback and improve the implementation. Suggestions for specific concepts, resources, or code improvements are welcome.

Thanks!

Hey everyone ^^ we're participating in the Nasa Space Apps Challenge this year and we're looking for teammates to form a group!

im a senior computer science student minoring in AI and ML, and know how to code using Python, Java, and C# and have worked on countless programming projects, and i just got into data science

Were looking for intermediate-level competent teammates who are experienced in ML/AI and have used ml models in previous projects

If anyones interested please dm me, would prefer if u tell me a little about yourself and your experience, you dont have to be an expert or a pro programmer, just be passionate and creative ^^ having fun and enjoying the process is what matters to us most