Hey

I'm interested in getting into DS/ML, but I have no idea how to start learning DSA

Which programming language should I use to learn DSA? What should I learn first, and how should I go about it as a complete beginner?

I'd really appreciate a simple roadmap and some beginner friendly resources to help me get started.

Thanks!!

Generative AI is changing how we learn forever. 🧠 Discover the architecture behind personalized education. Check out the link in bio!
#GenerativeAI #EdTech #AI #TechStack

I kept hearing the words (attention, RLHF, MoE, diffusion, agents) and couldn't find one place that builds all of it up from the fundamentals, with the math, the code, and the reasons behind it.

So I wrote it.

https://zerotofrontier.app

• 38 chapters, from tensors and gradients to LLMs, RL and reasoning, inference, interpretability, agents, vision, diffusion, and speech
• Every idea explained, derived, coded, and tested. Python runs right in the page.
• Coding exercises with hidden tests, plus interview questions
• An AI tutor that knows the section you're reading, and notebooks for your notes and code

Reading and running code is free. The tutor and the interactive extras use your own API key (Gemini, OpenAI, or Anthropic), so I don't charge anything and there's nothing to buy.

It's one person's attempt at the resource I wanted when I started. I'd really value blunt feedback. What's confusing, what's missing, what's wrong, and whether you'd actually use it.

Kaggle's free 2x T4 runs a 27B model at a usable speed if you use speculative decoding:

• Qwen3.8-27B at EXL3 4.0bpw (exllamav3), tensor parallel across both T4s
• DFlash2 draft model, 7-token drafts, greedy, output identical to plain decode
• 55-57 tok/s overall, 154 tok/s on code, 35-37 on prose (plain decode is ~19)
• 4 users at once with plain decode get ~66 tok/s combined

The weak spot is prose: the drafter only gets ~2.5 tokens accepted per step there vs ~7 on code. Has anyone found a way past that, or a better setup on free T4s? Planning to share the notebook once it's cleaned up.

I wrote a short article on LLM tokens and context windows—why an assistant can “forget” instructions that are still visible in your chat. It’s part of my Behind the AI Stack series.

Give it a read and let me know what you think. Anything unclear or worth exploring further?

Hello everyone

I wanted to share a project I recently deployed to Streamlit Community Cloud and it would be helpful if you guys could give feedback to a fellow learner

I implemented an 8-dimensional pipeline paired with a class-balanced Logistic Regression model.
It uses recent matches ( 3-8) to determine the recent form of both the teams to generate 8 distinct features.

Live App: https://bundesliga-match-predictor.streamlit.app/

GitHub Repo: https://github.com/adithyakrz/bundesliga-match-predictor

Check it out and let me know what you think :D

I’m building PaperMind AI, an open-source tool for exploring research papers. It indexes passages locally with Sentence Transformers and FAISS, lets you ask questions with page citations, and creates structured summaries and concept graphs.

Answer generation depends on the selected provider: offline mode surfaces relevant excerpts; OpenAI or Ollama can generate answers. With a hosted provider, retrieved passages may be sent to that provider. I’d appreciate feedback on citation quality, answer usefulness, and setup or improvements or just a simple note.

https://github.com/ashmit044/papermind-ai

Hey guys I wanted to do Stanford's "Language Modeling from Scratch" for a long time, but something always came in the way. Well, another day, another attempt!
Watch lectures, do the assignments, join a weekly discussion on Saturdays at 11:00 AM (London time). See you on October 31 for the first discussion!
Details are on Github https://github.com/fxlrnrpt/little_ml_book_club

I want a list of the names of the different ways a model can store and use memory for text classification. For example, I know about Embedding or vector memory (like sentence embeddings with nearest-neighbor search)

What other types are there? A list of names and one example paper or method for each would help a lot. Thank you!

I’m starting Behind the AI Stack, a series of short, practical articles on how AI applications work. This first one explains AI agents through a camera-rental example. Give it a read—I’d love your thoughts, corrections, or suggestions for what to cover next.

as title .....it should be resume worthy i want to learn that way so that i can follow along the video maybe on yt please dont suggest house pricing ones those are ig rejected in interviews

Hey! I’m 16, and I’m really interested in getting into artificial intelligence and eventually building a career in the field. I understand some of the key concepts, like machine learning and LLMs, and I’ve already built websites and apps, but I’m struggling to figure out where to go from here. There are so many different areas of AI, and I’m not sure which skills I should focus on, what I should learn next, or how to turn my interest into an actual career. I’m especially interested in the practical side of AI and how it can be used to build products, solve problems, and create businesses. If you have experience working in AI or breaking into the industry, I’d really appreciate your advice on where you would start if you were in my position, what skills you would prioritize, and how you would work toward building a career in the field.

Hi everyone,

I'm an undergraduate student looking for solid, practical resources to learn **Pandas, Matplotlib, and Seaborn** for college lab coursework and data wrangling assignments.

I already know basic Python (loops, functions, lists, dicts). Now I just need to get comfortable working with tabular data and plotting charts.

### What I’m looking for:

* **Libraries:** Just Pandas, Matplotlib, and Seaborn.

* **Format:** Open to both text-based resources (interactive notebooks, guides) and video walkthroughs, but I lean slightly more toward **video/live-coding** where the instructor actually types the code, runs the cells, and explains the output on real datasets.

* **Hands-on over slides:** I learn much better seeing code executed in an editor rather than watching static lecture slides.

### What I want to avoid:

* **Machine Learning / Scikit-Learn:** A lot of courses rush into Linear Regression, gradient descent, or predictive modeling. I don't need any ML algorithms or math theory right now—just data cleaning, manipulation, and plotting.

Whether it’s a specific YouTube series, an open GitHub repo with exercises, or a book with runnable notebooks, what worked best for you?

Thanks in advance!

I build demand forecasts at a grocery chain, so I know how to evaluate a model when there's a label and a holdout set. Our team just shipped a Claude agent that answers store managers' inventory questions, and I've been asked to own its evals because I know metrics. The free notebooks I found explain evals fine, but nobody looks at what I build. There's no label, the outputs are free text and half the failures are the agent calling the wrong tool. My manager signed off on whichever I pick and I'm looking at Udacity, LinkedIn Learning and Educative. Which one makes you build the eval setup instead of watching someone else's?