Seq2seq-PyTorch VS Deep-Learning-Papers-Reading-Roadmap

Found relevant code at https://github.com/b-etienne/Seq2seq-PyTorch + all code implementations here

Code for https://arxiv.org/abs/1508.04025 found: https://github.com/b-etienne/Seq2seq-PyTorch

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.

Deep Learning Papers Reading Roadmap

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?

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.

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.