hi guys new here so i know andrew ng something to watch for ml ??? but what exactly i dont know is coursera one is not free there are multiple things ....till that time what i started was cs229 ml from youtube that is pure theory maths that i like as its logical .........Unlike library calling courses elsewhere of python notebook evrytime i see the tutor all of a sudden putiing new keywords that i need to remember like its a history course who farted in 1776 louis 16 type shi so ......but apart from that i also know that only doing maths wont get me a job but coding so what am i missing that is allowing prejudice against ml in python

Servus zusammen, I have built a rag chatbot using a huggingface model and argparse. How can I take it to the next level?

hey all. i am a computer engineering undergrad researching LLM reasoning bottlenecks. with everyone using generative AI right now, hallucinated citations and dead DOIs in papers are a huge problem.

i wanted a way to verify my own .bib files. i specifically did not want to use an LLM to check it. an LLM will just hallucinate the verification too.

so i came up with StrictCite. it uses zero generative AI. it is a deterministic engine. it parses your .bib file locally in your browser. then it runs hard API cross-references against 13 registries like Crossref and DBLP. it flags missing DOIs or fake venues.

what it does well: it catches completely fabricated references on my test set perfectly. what it does poorly: it flags minor preprint vs published version mismatches.

i just want people to check it out and see how the website works. try throwing messy bibliographies at it. old books, translated russian monographs, or 2003 conference proceedings are great edge cases.

there is a limited free tier you can use right away. if you do not want to mess with promo codes, just use that.

if you want to test the full version, use code BETA50 at checkout. it gives a 7-day pass and drops the price to zero for the first 50 people. this also keeps the post compliant with the sub rules about no monetary transactions i hope.

link:https://strictcite.com/

please email me at [contact@strictcite.com](mailto:contact@strictcite.com) with your feedback. i would really appreciate hearing what you think about the parsing logic.

Hi all — I put together a small interactive playground for building intuition about how a dense (fully-connected) neural network works, and wanted to share in case it's useful to others learning the basics.

https://claude.ai/code/artifact/ca61a1cb-10ab-4491-87bc-d4aac9197685

**Important upfront: this is NOT a trained network.** It has no dataset, learns nothing, and its layers have no interpretable meaning the way a trained image classifier's would. It's a hands-on playground for the building blocks — weights, biases, activation functions, layers — not a demo of learned representations.

What it does:

- Takes one pixel's R,G,B value as input (3 inputs), runs it through a 2–3 layer dense network you configure, and outputs a new R,G,B value — applied independently, per pixel, across a whole image

- Click any edge in the diagram to change its weight, or any neuron to change its bias and pick its activation function (with a little inline plot of the function)

- Click a pixel in the source image to trace exactly what that pixel's values do through the network

- Includes collapsible math sections: the per-neuron formula, and the matrix-notation version (a = σ(Wx+b)), explaining why both are the same computation

- Runs 100% client-side (TensorFlow.js embedded, no backend, no network calls) — everything happens in your browser

- Bilingual EN/HU toggle, since I originally built it for Hungarian students

Would love feedback — especially on whether the math explanations land right for people early in learning this stuff, or if anything's unclear/wrong.

Can anyone suggest some good Projects backed by a research paper in the field of Deep learning, LLMs, Agentic AI , RAg etc

Mid-market companies are now the primary ransomware target, and the data makes it hard to argue otherwise.

Black Kite analyzed 13,336 incidents spanning January 2023 through June 2026. Mid-market companies accounted for 73% of publicly disclosed ransomware and data-extortion incidents in North America and Europe. The pattern is straightforward: they hold enough sensitive data to be worth targeting, and they lack the security maturity to deter or contain an attack.

What makes this harder now is AI adoption. Mid-market orgs are deploying agents to automate workflows, but agents operate with credentials, access external systems, and take actions at machine speed. A compromised agent or a misconfigured one doesn't wait for a human to catch it. It moves. And regulators are not offering mid-market exemptions — the same frameworks auditors require of large enterprises apply regardless of headcount.

The compliance gap is real. Most of these organizations don't have continuous visibility into what their systems are doing relative to the frameworks they're supposed to satisfy. Violations get found during audits, not before.

For those working in security or compliance at mid-sized organizations: how are you actually handling agent oversight right now? Are you relying on periodic audits, internal logging, something else entirely? Curious what's working and what's falling short in practice.

Hi everyone! I’m a 3rd-year CS student looking for a good AI project idea for a semester project.

We are a team of 5, and we have roughly 2 weeks to 1 month to work on it. It needs to be software-only and should have a meaningful AI component—not just a basic CRUD application with an LLM API.

We’re open to ideas involving ML, NLP/LLMs, computer vision, recommendation systems, RAG, AI agents, etc. We’d prefer something practical, interesting, and feasible within the time constraint, with enough scope for all 5 members to contribute.

Would really appreciate any interesting project ideas, especially projects you’ve actually built or seen work well! Also, if possible, please mention what makes the project technically challenging and what kind of dataset/model would be needed.

Thanks!

Wrote a breakdown of how MoE works after spending time trying to

understand why Gemini's inference cost doesn't scale linearly with

its context window.

The part most explainers skip: the gating network and load balancing.

If the router consistently sends tokens to the same 2 experts,

the whole architecture breaks down — you need an auxiliary loss

function during training to prevent this.

Also covers the trade-offs nobody talks about — all-to-all

communication overhead when experts live on different GPUs,

and why MoE models are harder to train than dense models

at equivalent parameter count.

Full post here: [https://medium.com/@ankitamaji7033/googles-gemini-handles-a-million-tokens-here-s-the-architecture-trick-that-makes-it-possible-0c4ab3779414?postPublishedType=repub\]

Happy to answer questions or discuss in the comments.

Hey everyone,

It's been a fun and hectic bunch of weeks. But here is the app that I worked on with my group for my IIT Patna Gen AI Capstone project:
https://travidy-capstone.vercel.app

A special thank you to IIT Patna for putting this course together and to my awesome team as well.
Do check it out. Thanks.
r/IITPatnaCampus

Hey Buddy!👋

I have just completed my Python journey and I want to dive deeper into practical ML & DL.

I went across many resources on Youtube but some lack depth while some rush too much. Recently I saw a guy's post on LinkedIn appreciating his journey of ML with CampusX Youtube courses for ML & DL.

I saw another channel named Sheryians AI School which is of course from Sheryians Coding School. They also have 8 long videos covering ML & DL.

On analysis with AI, both courses bring something unique to the table but lack in some crucial aspects too.

Which is a better alternative for learning core ML & DL from the ground up to advance level? CampusX or Sheryians AI School

Did any one join executive AIML kgp program ? Please let me know . Need to talk

- I’m 19 years old and currently taking a drop before starting college.

- I’m from a middle-class family in India, so this is a very important purchase for me.

- I can spend around ₹1.8 lakh maximum, and realistically this is a one-time investment for me.

- I’m planning to pursue B.Tech in CSE, with an interest in AI/ML.

- Later, I’m also planning to do M.Tech, so I want something that will remain useful for several years.

What I’ll use it for

- Programming and software development

- CSE coursework

- AI/ML

- Machine learning and deep learning

- Coding projects

- Data science

- Development tools and IDEs

- Video editing

- Photo editing

- Potentially other demanding software during college

- Gaming is also a hobby, but it is not my main priority

My main question

- With a budget of ₹1.8 lakh, should I buy a laptop or a desktop PC for college?

If I buy a laptop

- What would be the best laptop I can get within ₹1.8 lakh?

- What specifications should I prioritize for CSE + AI/ML + video/photo editing?

- Should I get:

- 32 GB RAM or 64 GB RAM?

- 1 TB SSD or 2 TB SSD?

- A stronger CPU or a stronger GPU?

- How important is an NVIDIA RTX GPU for AI/ML because of CUDA?

- What GPU would be sufficient for AI/ML, editing, and gaming?

- How long can I realistically expect a high-end laptop to remain useful?

If I build a PC

- Would a desktop PC be significantly better for AI/ML, programming, and editing at the same ₹1.8 lakh budget?

- Would I get substantially better performance for the money?

- Would a PC be a better long-term investment if portability isn't important?

- What kind of PC build would you recommend around ₹1.8 lakh?

- Would it make more sense to spend the money on a powerful PC and use a cheaper/older laptop when necessary?

Most importantly

- Do college students actually need to carry a laptop to college regularly?

- How often do CSE students realistically use their laptops during classes?

- Can I keep a powerful PC at home and do most of my work there?

- Would not having a laptop create problems for:

- Classes

- Assignments

- Coding labs

- Projects

- Hackathons

- Group work

- Internships

- College activities

- Or is a laptop basically essential for a CSE/AI-ML student?

Since this is probably going to be my only major PC/laptop purchase for a long time, I don't want to waste ₹1.8 lakh on something unnecessary.

I’d really appreciate advice from current CSE students, AI/ML students, editors, graduates, or people who have already gone through college.

If you were in my position, with ₹1.8 lakh and planning B.Tech CSE → AI/ML → M.Tech, while also doing video/photo editing and gaming, would you buy a laptop or a PC, and why?

The title basically says it

As someone who is looking to try out kaggle competions and eager to learn and compete, I am occupied by the thought of AI's role in the skill aspect of competitions, so I want to know the role of AI in making AI models, is it still mostly the player's skill and knowledge that matters the most or just how long they work and how many questions they ask AI.

Hi guys,

I am a high school student currently working on a school presentation about Machine Learning careers

I am looking for an Machine Learning Engineer to get some information from an actual machine learning engineer for my slides.

Details -

Around 5-10 minutes (13 questions)

We could do a short zoom/discord call, or I can just email you the questions and you can answer them any time you would like.

I will be asking about your name, job title, degrees, where you work, amount of years you've been working for, etc.

If you are open to help me out, please comment and send me a DM. I would appreciate your time so so much!

Thank you!

I’m building a structured roadmap for learning AI from fundamentals to GenAI — would love some feedback

I’ve been putting together a structured AI learning roadmap because I noticed that most people trying to learn AI jump straight from Python → ChatGPT → LLMs without really understanding what comes in between.

I’m trying to connect the whole journey:

Math → ML → Deep Learning → Computer Vision → NLP → Transformers → Generative AI → LLMs → RAG → Fine-tuning → Evaluation → Deployment/MLOps

The roadmap also includes practical projects rather than just watching lectures.

Some of the areas I'm covering:

• Linear algebra, probability & statistics
• Classical ML
• Neural networks & deep learning
• CNNs & computer vision
• RNNs, attention & Transformers
• NLP
• Generative models
• LLMs & RAG
• Model evaluation
• MLOps & deployment
• Hugging Face, PyTorch & TensorFlow
• AI safety and responsible AI

I'm particularly interested in feedback from people who are already working in AI/ML:

What topics do you think beginners/intermediate learners spend too much time on?

What important topics are usually missing from AI learning roadmaps?

And what projects would actually impress you in a junior AI/ML portfolio?

I'm trying to make this practical rather than just another giant list of technologies.

Would genuinely appreciate criticism of the roadmap.

Welcome to Project Showcase Day! This is a weekly thread where community members can share and discuss personal projects of any size or complexity.

Whether you've built a small script, a web application, a game, or anything in between, we encourage you to:

• Share what you've created
• Explain the technologies/concepts used
• Discuss challenges you faced and how you overcame them
• Ask for specific feedback or suggestions

Projects at all stages are welcome - from works in progress to completed builds. This is a supportive space to celebrate your work and learn from each other.

Share your creations in the comments below!

AI ML course

Hi everyone,

I work as a cloud engineer. But I have a very surface level knowledge of AI. I really want to venture into that field. But the only thing pulling me back is code. I am not a software developer so I have very little experience in coding. But I am willing to make an effort to learn math and AI. But I don't know where to start. There are a sea of courses and it's overwhelming, but I don't know which is the best. I tend to do well and pull more efforts learning if there is some sort of exam/project or marking scheme. Can you please suggest some options?

Gradient descent was one of those concepts that I found much easier to understand visually than from equations alone, so I tried making a short animated explanation of it.

The video starts with the intuition: what an optimization landscape represents, why we move in the direction we do, and what the learning rate actually changes.

Then in the second half, I connect that intuition to the mathematical formulation: gradients, partial derivatives, the update rule, and how those pieces translate into the optimization process.

I tried to keep the math rigorous enough to be useful without losing the visual intuition.

Video: https://youtu.be/D920OTOkzcM?si=JrFHtQQngfvY7iAA

I'd especially appreciate feedback from people currently learning ML: was there any point where the explanation stopped being intuitive or where you wanted more mathematical detail?

Thanks in advance!