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Attention and Transformers from Scratch

Attention and Transformers from Scratch
Total Titles: 32
 
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DEMOS
01_001 - What AI can do with language
01 - Course Introduction - Attention and Transformers from Scratch
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31.05.17
01_002 - Why attention
01 - Course Introduction - Attention and Transformers from Scratch
19 View(s)
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31.05.17
01_003 - The NLP engineer roadmap
01 - Course Introduction - Attention and Transformers from Scratch
16 View(s)
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31.05.17
02_004 - Opening questions and the Seq2Seq idea
02 - Limits of RNN Seq2Seq - Attention and Transformers from Scratch
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31.05.17
02_005 - How a Seq2Seq model is implemented
02 - Limits of RNN Seq2Seq - Attention and Transformers from Scratch
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31.05.17
02_006 - The problem with a fixed context vector
02 - Limits of RNN Seq2Seq - Attention and Transformers from Scratch
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31.05.17
02_007 - The arrival of attention
02 - Limits of RNN Seq2Seq - Attention and Transformers from Scratch
14 View(s)
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31.05.17
03_008 - attention_impl_EN.ipynb.bin
03 - Building a Seq2Seq Model - Attention and Transformers from Scratch
20 View(s)
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31.05.17
03_008 - Data, encoder and decoder in PyTorch
03 - Building a Seq2Seq Model - Attention and Transformers from Scratch
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31.05.17
03_009 - Training setup and the first run
03 - Building a Seq2Seq Model - Attention and Transformers from Scratch
13 View(s)
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31.05.17
03_010 - Does a wider hidden state help Sweeps
03 - Building a Seq2Seq Model - Attention and Transformers from Scratch
16 View(s)
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31.05.17
04_011 - The core idea of attention
04 - The Attention Mechanism in Depth - Attention and Transformers from Scratch
12 View(s)
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31.05.17
04_012 - Bahdanau attention in detail
04 - The Attention Mechanism in Depth - Attention and Transformers from Scratch
14 View(s)
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31.05.17
04_013 - Implementing Bahdanau attention
04 - The Attention Mechanism in Depth - Attention and Transformers from Scratch
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31.05.17
05_014 - attention_impl_EN.ipynb.bin
05 - Implementing Attention - Attention and Transformers from Scratch
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31.05.17
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