There is no shortage of AI content online, but over time I have found myself returning to the same handful of resources again, and I wanted to share the three that have helped me the most.
This one I would recommend to anyone who is earlier in their journey. AI Summer at theaisummer.com is a free platform run by Sergios Karagiannakos and Nikolas Adaloglou, and it covers everything from the basics of neural networks through to building and deploying real ML systems. The tone is friendly and practical, and there are proper code examples throughout. It is one of those rare resources that manages to be beginner-friendly without feeling watered down.
Lilian Weng has been publishing at lilianweng.github.io since 2017, and i have found most posts to be a long, comprehensive review into a topic. I have used her writing to get my head around diffusion models, reinforcement learning, and large language models, among plenty of other things. What I love about it is that she does not dumb things down, but she also never loses you in jargon. I always come away feeling like I actually learned something.
Sander is a research scientist at Google DeepMind who has worked on some generative AI projects and his blog at sander.ai reflects that experience. The posts are detailed and mathematically rigorous, but also enjoyable to read. I first found it when trying to understand diffusion models properly, and his explanations clicked in a way that a lot of other writing on the topic did not. If you are interested in generative models specifically, this is essential reading.
These are all resources I have used myself and recommend. If you are looking for places to start learning, I cannot point you anywhere better.
