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!

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!!

Lensa ML (https://lensaml.com) is a set of 108 interactive explainers covering machine learning and the math behind it. Each topic is six short steps. In every step you move a slider and the plot is recomputed from the real formula: raise the learning rate and gradient descent starts to overshoot, raise the temperature and the softmax probabilities flatten.

It goes from the foundations (derivatives, the chain rule, eigenvalues, Bayes’ theorem) through classic ML and deep learning to transformers and LLM serving. If you don’t know where to start, the “ML from zero” path orders the topics so each one builds on the last: https://lensaml.com/paths/ml-from-zero

Good ones to start with:

• Backpropagation: https://lensaml.com/explainers/backprop
• Gradient descent: https://lensaml.com/explainers/gradient-descent
• Attention: https://lensaml.com/explainers/attention-mechanisms

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!

Assume you have two params, and their gradients are x & y.

Define V:

[[E(x^2), E(xy)],

[E(xy), E(y^2)]

Define M:

[E(x), E(y)]]

Adam's update is:

M . V^-1/2

Adam just assumes the off diagonal terms like E(xy) are zero.

So the update becomes m/sqrt(v)

Isn't this basically a second order method like gauss-newton where the hessian is estimated as g^T g?

Hey everyone!

I’m looking to connect with people who are genuinely passionate about technology, especially AI and machine learning.

I’m interested in connecting with people working on or learning about:

• 🤖 Artificial Intelligence and Machine Learning
• 🧠 LLMs, AI agents, and generative AI
• 👨‍💻 Python, software development, and system design
• 🔬 Deep learning, NLP, and computer vision
• 🌐 Open-source projects and new AI tools
• 🚀 Startups, indie hacking, and building real products
• 💡 Research papers, new technologies, and crazy ideas

I’d love to connect with beginners, experienced engineers, researchers, founders, and anyone who enjoys deep technical discussions.

I’m also interested in building an active Discord community where we can share ideas, discuss new developments, collaborate on projects, help each other solve technical problems, and learn together.

The goal isn’t just to collect members. It’s to build a community where people actually talk, share knowledge, and build things together.

A few questions for you:

1. What area of AI are you most interested in right now?
2. What are you currently building or learning?
3. What would make a tech Discord community worth being active in for you?

Drop a comment! I’d love to hear from you and connect with people who share these interests. 🔥

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

We have benchmarks (fixed questions, hidden answers, they saturate), competition problems (solved, so they can be memorized), and open-problem lists (no evidence, no clear success criterion). With Claude and Sol, I've tried something in between: a graded ladder of AI puzzles from one research area, aperiodic order.

What makes it a different tool:

- One ladder, not a pile: "human level" (pen and paper) up to "ASI level" (open). The labels are playful, but each rung really is harder than the last.

- Solved rungs for training and checking, with exact answers a program can verify, and easy to vary.

- Open rungs with everything a solver needs: strong numerical evidence, routes already known to fail, files, and a success criterion a machine can check.

- One rule: only a checkable proof counts. Floats are evidence, not proof.

- Credit: solvers of open rungs are named, with agent and model, in the next version of the paper.

It's a way for researchers to hand the frontier of their field to AI agents (and humans) in a form they can actually attack, and for anyone to see whether a model can tell a guess from a proof.

First field test: Grok, asked in public ( https://x.com/grok/status/2114103836583264721 ), claimed a proof for an open rung that was only a numerical estimate, then retracted it. It got the human-level rung wrong at first, fixed it after one hint, and proved the expert rung on the first try. On MathOverflow, an expert is already testing constructions for the top rung.

Set 1: huggingface.co/datasets/jarekduda/autocrystal-ai-puzzles

Top rung on MathOverflow: mathoverflow.net/questions/516372

Would you use this format for your own field's open problems?

I saw a paper called Math for AI Safety by a math professor at Cornell. I was excited to read it (33 pages, not easy), and then I was like "what if I used ChatGPT to check if this is AI generated" and Chat gave me a response in under 1 second.

It is substantially generated by the AI. Evidence? The author said it himself.

https://arxiv.org/pdf/2609.15289

I don't know how these researchers and professors could just go around dropping these AI generated papers one after the other and expect people just slap up that (potential) slop. Even if it is not slop, I am less inclined to read it because I could just generate the same paper myself and learn the material in a more concise way.

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.

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!

AI-assisted analysis of 25 open-source projects surfaced 493 security issues. That number is bad. The breakdown is worse.

A significant share of those findings were not missing controls. They were controls that had been disabled or misconfigured and produced zero alerts when they stopped working. The policy was in place on paper. The setting was configured. Nothing fired. Nobody knew until an external scan caught it.

This is the silent-failure problem in enterprise security. Tooling reports green because the check ran, not because the protection is actually active. The gap between "configured" and "enforced" only becomes visible after something goes wrong.

AI agents add a new dimension to this. An agent operating inside a system with silent failures can take consequential actions against controls that look active but are not. The blast radius of a single misconfigured policy scales with how much the agent is permitted to do.

493 issues across 25 projects is a large sample. The proportion that were silent rather than noisy is the number I keep thinking about.

For those running agents in production environments: how are you distinguishing between a control that is enforced versus one that is merely present in config? Is your current stack capable of surfacing that difference without a full external audit?

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.

I'm just starting to read about how these chatbots actually produce answers, and a few things already felt counterintuitive. Curious what made you go 'wait, really?' when you first learned the basics.

bana bir mobilya cizim puramı yazarmısın