Keeping the pace of current Deep Learning Research is almost impossible and even focusing on a specific field, like Computer Vision, is extremely hard because of the new papers rate (which by the way I think it is a great thing, it makes us living in exciting times), their hard to understand content and the fact code is not so often accompanying the paper
This is specifically true if you are student as, in my opinion, the gap between the educational material (books, courses, ...) and the research material today is quite big.
The goal of this repo is to try helping students to make the quantum leap to get from the students side to the researchers side with the following methodology
- Selecting relevant or interesting papers
- Providing papers summaries
- Adding code snippets implementing specific parts
- Diving a bit more into theory and math when necessary (research papers tend to be vague on this kind of things)
As explained in Contribute.md contribution are absolutely welcome
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Open issues to
- bugfix: notify me about bugs, typos, ...
- features: suggest relevant papers, discuss content, ...
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Submit PR with new summaries, implementations, ...
Feel free to DM me on Twitter ar @NicolaBernini to discuss anything
Added a PR about Statistics and the first summary regards Variational Bayesian Monte Carlo also published as Kaggle Kernel
Added a PR to add D2 Net Summary
Added a PR to define the CNN in Computer Vision - Section starting with a Paper Summary