I recently built (and am continuing to improve) intuitivepapers.ai to help me study and understand AI research papers.
For me, it's important to build intuition for concepts, and when reading a research paper, that can take me a while. Also, papers can be unnecessarily intimidating or verbose. I also find myself having to jump around to prior papers to understand the preceding work. My motivation was to be able to read an explainer in one place that:
- explains the preceding foundations required to understand the paper
- provides intuition
- uses plain language where possible
- provides concrete implementation examples, so I can understand how the idea is actually implemented in practice
- cross references the paper against accompanying source code
I originally started building this as something for myself, but I thought others might find this helpful too.
New paper explainers are published regularly. There is a queue where you can submit and upvote papers for explaining.
At the bottom of each explainer is a feedback form where you can suggest improvements. I will incorporate these into already published explainers, but I will also incorporate the lessons into future posts as well.
Looking forward to everyone's feedback, and I hope you find this helpful!
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Show HN: A website to understand and study AI papers · HackerTrans
I recently built (and am continuing to improve) intuitivepapers.ai to help me study and understand AI research papers.
For me, it's important to build intuition for concepts, and when reading a research paper, that can take me a while. Also, papers can be unnecessarily intimidating or verbose. I also find myself having to jump around to prior papers to understand the preceding work. My motivation was to be able to read an explainer in one place that:
- explains the preceding foundations required to understand the paper - provides intuition - uses plain language where possible - provides concrete implementation examples, so I can understand how the idea is actually implemented in practice - cross references the paper against accompanying source code
I originally started building this as something for myself, but I thought others might find this helpful too.
New paper explainers are published regularly. There is a queue where you can submit and upvote papers for explaining.
At the bottom of each explainer is a feedback form where you can suggest improvements. I will incorporate these into already published explainers, but I will also incorporate the lessons into future posts as well.
Looking forward to everyone's feedback, and I hope you find this helpful!