- [Resource] A top level overview of major deep learning architectures https://github.com/johnGettings/Deep_Learning_Architectures 4 comments artificial
- [Resource] A top level overview of major deep learning architectures https://github.com/johnGettings/Deep_Learning_Architectures 3 comments learnmachinelearning
Linked pages
- [1706.03762] Attention Is All You Need https://arxiv.org/abs/1706.03762 145 comments
- Part 2: Kinds of RL Algorithms — Spinning Up documentation https://spinningup.openai.com/en/latest/spinningup/rl_intro2.html#a-taxonomy-of-rl-algorithms 11 comments
- [1506.02640] You Only Look Once: Unified, Real-Time Object Detection http://arxiv.org/abs/1506.02640 8 comments
- [1406.2661] Generative Adversarial Networks https://arxiv.org/abs/1406.2661 7 comments
- [1812.04948] A Style-Based Generator Architecture for Generative Adversarial Networks https://arxiv.org/abs/1812.04948 6 comments
- [1512.03385] Deep Residual Learning for Image Recognition http://arxiv.org/abs/1512.03385 6 comments
- Illustrated Guide to LSTM’s and GRU’s: A step by step explanation | by Michael Phi | Towards Data Science https://towardsdatascience.com/illustrated-guide-to-lstms-and-gru-s-a-step-by-step-explanation-44e9eb85bf21 0 comments
- https://cdn.openai.com/papers/dall-e-2.pdf 0 comments
- [1409.4842] Going Deeper with Convolutions http://arxiv.org/abs/1409.4842 0 comments
- [2010.11929] An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale https://arxiv.org/abs/2010.11929 0 comments
- [1703.10593] Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks https://arxiv.org/abs/1703.10593 0 comments
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