Deep-Learning-Papers-Reading-Roadmap
Deep Learning papers reading roadmap for anyone who are eager to learn this amazing tech! (by floodsung)
seq2seq
Attention-based sequence to sequence learning (by alex-berard)
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| Deep-Learning-Papers-Reading-Roadmap | seq2seq | |
|---|---|---|
| 5 | 1 | |
| 37,120 | 390 | |
| - | - | |
| 0.0 | 0.0 | |
| over 1 year ago | almost 5 years ago | |
| Python | Python | |
| - | Apache License 2.0 |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Deep-Learning-Papers-Reading-Roadmap
Posts with mentions or reviews of Deep-Learning-Papers-Reading-Roadmap.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2021-09-08.
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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.
seq2seq
Posts with mentions or reviews of seq2seq.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2021-09-08.
-
[D] Resources for Understanding The Original Transformer Paper
Code for https://arxiv.org/abs/1409.0473 found: https://github.com/eske/seq2seq
What are some alternatives?
When comparing Deep-Learning-Papers-Reading-Roadmap and seq2seq you can also consider the following projects:
faceswap - Deepfakes Software For All
tensor2tensor - Library of deep learning models and datasets designed to make deep learning more accessible and accelerate ML research.
Real-Time-Voice-Cloning - Clone a voice in 5 seconds to generate arbitrary speech in real-time
pytorch-seq2seq - Tutorials on implementing a few sequence-to-sequence (seq2seq) models with PyTorch and TorchText.
Seq2seq-PyTorch
Keras - Deep Learning for humans
Deep-Learning-Papers-Reading-Roadmap vs faceswap
seq2seq vs tensor2tensor
Deep-Learning-Papers-Reading-Roadmap vs Real-Time-Voice-Cloning
seq2seq vs pytorch-seq2seq
Deep-Learning-Papers-Reading-Roadmap vs pytorch-seq2seq
seq2seq vs Seq2seq-PyTorch
Deep-Learning-Papers-Reading-Roadmap vs Keras
Deep-Learning-Papers-Reading-Roadmap vs tensor2tensor
Deep-Learning-Papers-Reading-Roadmap vs Seq2seq-PyTorch