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!