seq2seq
Attention-based sequence to sequence learning (by alex-berard)
Deep-Learning-Papers-Reading-Roadmap
Deep Learning papers reading roadmap for anyone who are eager to learn this amazing tech! (by floodsung)
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seq2seq | Deep-Learning-Papers-Reading-Roadmap | |
---|---|---|
1 | 5 | |
390 | 37,120 | |
- | - | |
0.0 | 0.0 | |
almost 5 years ago | over 1 year 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.
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
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.
-
[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.
What are some alternatives?
When comparing seq2seq and Deep-Learning-Papers-Reading-Roadmap you can also consider the following projects:
faceswap - Deepfakes Software For All
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.
Keras - Deep Learning for humans
tensor2tensor - Library of deep learning models and datasets designed to make deep learning more accessible and accelerate ML research.
Seq2seq-PyTorch