Deep-Learning-Papers-Reading-Roadmap
Deep Learning papers reading roadmap for anyone who are eager to learn this amazing tech! (by floodsung)
pytorch-seq2seq
Tutorials on implementing a few sequence-to-sequence (seq2seq) models with PyTorch and TorchText. (by bentrevett)
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| Deep-Learning-Papers-Reading-Roadmap | pytorch-seq2seq | |
|---|---|---|
| 5 | 3 | |
| 37,120 | 5,150 | |
| - | - | |
| 0.0 | 5.4 | |
| over 1 year ago | 3 months ago | |
| Python | Jupyter Notebook | |
| - | MIT License |
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.
pytorch-seq2seq
Posts with mentions or reviews of pytorch-seq2seq.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2021-10-29.
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A Good Github Repo to Look at (CS388 Natural Language Processing)
I don't know how many people are taking CS388 NLP in Fall 2022, but the assignment is really putting lots of stress on me. I was searching some good materials to prepare for NLP class, and a really good resource to look at is this github repo: https://github.com/bentrevett/pytorch-seq2seq.
- [D] How to truly understand attention mechanism in transformers?
- [D] Resources for Understanding The Original Transformer Paper
What are some alternatives?
When comparing Deep-Learning-Papers-Reading-Roadmap and pytorch-seq2seq you can also consider the following projects:
faceswap - Deepfakes Software For All
Time-Series-Forecasting-Using-LSTM - Time-Series Forecasting on Stock Prices using LSTM