Difference between revisions of "Transformer"
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* [[Attention]] | * [[Attention]] | ||
* [http://medium.com/inside-machine-learning/what-is-a-transformer-d07dd1fbec04 What is a Transformer? | Maxime Allard - Medium] | * [http://medium.com/inside-machine-learning/what-is-a-transformer-d07dd1fbec04 What is a Transformer? | Maxime Allard - Medium] | ||
| − | * [[ | + | * [[Recurrent Neural Network (RNN)]] |
* [[Bidirectional Encoder Representations from Transformers (BERT)]] | * [[Bidirectional Encoder Representations from Transformers (BERT)]] | ||
* [[Natural Language Processing (NLP)]] | * [[Natural Language Processing (NLP)]] | ||
Revision as of 08:07, 30 June 2019
YouTube search... ...Google search
- Attention
- What is a Transformer? | Maxime Allard - Medium
- Recurrent Neural Network (RNN)
- Bidirectional Encoder Representations from Transformers (BERT)
- Natural Language Processing (NLP)
- Memory Networks
- Transformer-XL
- Tensor2Tensor (T2T) | Google Brain
- The Illustrated Transformer | Jay Alammar
- Sequence to Sequence (Seq2Seq)
Transformer Model - The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an Autoencoder (AE) / Encoder-Decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Attention Is All You Need | A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, and I. Polosukhin
