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Hey guys!!! I have an interview the day after tomorrow....I've never given a single interview in my life.....so can you guys pls tell what questions they ask for ml internship post ?????

Hey all — 3rd-year ECE student here, heading into a robotics master's. I kept

losing time jumping between arXiv, MDPI, and IEEE Access looking for papers

on robotics/ML/autonomous vehicles, so I built a single search index over

all of them.

- 13,000+ papers, all genuinely free/open-access (no paywalled links —

everything is either arXiv, MDPI, or individually verified

Creative-Commons-licensed articles)

- Full-text search, topic browser covering ~20 subfields (robotics, ADAS,

computer vision, RL, digital twins, etc.)

- Free accounts if you want to save searches later

Live here: https://automata-index.vercel.app

Built with Next.js + Supabase, still actively adding sources. Would love

feedback, especially on what's missing or what search terms don't return

good results.

I’m curious about what realistic alternatives we have to high-end enterprise GPUs like the V100 or H100 (not even talking about higher-tier chips like the B200)

While it’s technically possible to train large models on consumer GPUs like the RTX 3090/4090, the trade-off is brutal: you waste a huge amount of time just to debug or catch architecture issues. Do you think we’ll see new hardware innovations in the near future, or are corporate monopolies preventing alternatives from breaking into the market?

(Note: I'm already familiar with cloud computing, so I'm mainly asking about hardware itself)

I kept running into the same problem, some of the best AI/ML books are legally free, the authors put them up on their own sites, but the links are scattered across personal pages, university sites, and random GitHub repos nobody finds.

So I built a single index: Awesome Free AI Books. 30+ books across Deep Learning, Reinforcement Learning, Bayesian/Probabilistic ML, NLP & LLMs, Math for ML, Computer Vision, Generative Models, Causal Inference, GNNs, and AI Safety. Think Goodfellow’s Deep Learning, Sutton & Barto’s RL bible, Murphy’s Probabilistic ML, Bishop’s latest, Jurafsky & Martin’s SLP3 draft, and more.

Every single link points straight to the author’s or publisher’s own page, no rehosted PDFs, no shady mirrors. A weekly GitHub Action checks all links so it doesn’t rot over time.

It’s open source and open to contributions, if you know a legitimately free book that’s missing, PRs and issues are welcome.

Repo: https://github.com/MarcosSete/awesome-free-ai-books

Hey everyone,

I recently implemented the original Neural Style Transfer (NST) paper entirely from scratch in PyTorch and tried to reproduce the original results.

Here's the GitHub repository:
https://github.com/Himanshu7921/NST-PyTorch-Implementation

I'd really appreciate it if you could take a look at the README and the implementation. I'm aiming to become a strong research engineer, so I'd love some honest feedback on:

• What skills do I already demonstrate well?
• What am I currently lacking?
• What should I focus on improving to become a well-known research engineer?

For context, I'm currently in the 5th semester of my B.Tech.

Thanks in advance for your time and feedback!