Most people read ML papers wrong. They start at the abstract, hit the math, and give up halfway through.
I built FeynmanWiki to solve this issue.
Upload the PDF, get back a full illustrated deep dive with diagrams that actually explain what's happening.
Check it out at - https://www.feynmanwiki.com/
I was recently trying to understand how Mixture-of-Experts models scale without activating the full model every time. The main thing that confused me was routing and expert specialization, so I made a visual blog explaining DeepSeekMoE in a simple way. If you want any more deep learning blogs, drop a request in the comments and I’ll add them.
https://www.feynmanwiki.com/library/240106066v1-ki95
I was recently trying to understand how Mixture-of-Experts models scale without activating the full model every time. The main thing that confused me was routing and expert specialization, so I made a visual blog explaining DeepSeekMoE in a simple way. If you want any more deep learning blogs, drop a request in the comments and I’ll add them.
https://www.feynmanwiki.com/library/240106066v1-ki95
I was recently trying to understand how Mixture-of-Experts models scale without activating the full model every time. The main thing that confused me was routing and expert specialization, so I made a visual blog explaining DeepSeekMoE in a simple way. If you want any more deep learning blogs, drop a request in the comments and I’ll add them.
https://www.feynmanwiki.com/library/240106066v1-ki95
I was recently trying to understand how a world model can act like a zero-shot policy instead of needing separate policy training. The idea sounded simple but most explanations were hard to visualize, so I made a blog explaining the DreamZero approach with diagrams. If you want any more AI paper blogs, drop a request in the comments and I’ll add them.
https://www.feynmanwiki.com/library/wam-vgoz
I was recently trying to understand how Mixture-of-Experts models scale without activating the full model every time. The main thing that confused me was routing and expert specialization, so I made a visual blog explaining DeepSeekMoE in a simple way. If you want any more deep learning blogs, drop a request in the comments and I’ll add them.
https://www.feynmanwiki.com/library/240106066v1-ki95
I was recently trying to understand how Mixture-of-Experts models scale without activating the full model every time. The main thing that confused me was routing and expert specialization, so I made a visual blog explaining DeepSeekMoE in a simple way. If you want any more deep learning blogs, drop a request in the comments and I’ll add them.
https://www.feynmanwiki.com/library/240106066v1-ki95
I was recently trying to understand how Mixture-of-Experts models scale without activating the full model every time. The main thing that confused me was routing and expert specialization, so I made a visual blog explaining DeepSeekMoE in a simple way. If you want any more deep learning blogs, drop a request in the comments and I’ll add them.
https://www.feynmanwiki.com/library/240106066v1-ki95
I was recently trying to understand how Mixture-of-Experts models scale without activating the full model every time. The main thing that confused me was routing and expert specialization, so I made a visual blog explaining DeepSeekMoE in a simple way. If you want any more deep learning blogs, drop a request in the comments and I’ll add them.
https://www.feynmanwiki.com/library/240106066v1-ki95
I was recently struggling to understand what embeddings actually “mean” beyond just being lists of numbers. The main problem was that most resources explain the idea abstractly, so I made a visual blog that connects embeddings, similarity search, and vector databases in one flow. If you want any more beginner-friendly ML blogs, drop a request in the comments and I’ll add them.
https://www.feynmanwiki.com/library/vector-embeddings-and-vector-databases-aolz
I was recently struggling to understand GRPO and how RL is applied on LLM's, the main problem was not the resources but the lack of visual explanations, so I generated a blog for you guys that has both. If you want any more blogs on RL topics then drop a request in the comments and I will add them.
https://www.feynmanwiki.com/library/grpo-and-rl-for-llms-vogl
Over the past few months, I have been just trying to rack my brains to understand the intuition behind diffusion and flow matching. The youtube lectures were too shallow and the written resources had too much depth for me to focus.
Then I realized that the main problem is not text but visual, I had to imagine everything visually that was happening to get the correct intuition so I just consolidated all of my ideas and generated diagrams from chatgpt. Using this framework I created a visual blog to help me understand Diffusion and Flow matching.
I want to share the resource with you so you guys can also benefit from it.
Here it is
https://www.feynmanwiki.com/library/diffusion-and-flow-matching-aq77