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Deep-Learning-Papers-Reading-Roadmap reviews and mentions
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[D] Resources for Understanding The Original Transformer Paper
https://github.com/floodsung/Deep-Learning-Papers-Reading-Roadmap - This one is a bit dated so it doesn’t contain all of the papers that you need to read to get up to date but I think you should definitely read all of the papers in this list and implement as much as you can.
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4 ML Roadmaps to Help You Find Useful Resources to Learn From
Deep Learning Papers Reading Roadmap
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Should I implement every famous DL paper? [D]
I found a really great list of introductory and popular dl papers (github.com/floodsung/Deep-Learning-Papers-Reading-Roadmap) and I would absolutely implement every paper on this list if I had the time (at least a mini version e.g. CIFAR10 instead of ImageNet). Is is essential for me to implement every single paper on that list to become a good DL researcher and to start reading/implementing more recent ones? All the papers on the list are from before 2017 and I can't wait to start exploring the latest research! Would I be able to get away with just implementing a handful of papers from that list?
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[D] How did you implement papers with models that required a lot of GPUs to train?
I'm self-learning ML and trying to implement the papers listed here but I don't have access to hundreds of free GPUs like those corpos do.
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Looking for Beginner CV Resources
Definitely check out this list https://github.com/floodsung/Deep-Learning-Papers-Reading-Roadmap It's all papers, you should get used to reading scientific material.
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over 1 year ago
The primary programming language of Deep-Learning-Papers-Reading-Roadmap is Python.
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