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swethatanamala.github.io | ||
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jalammar.github.io
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| | | | | Translations: Chinese (Simplified), French, Japanese, Korean, Persian, Russian, Turkish, Uzbek Watch: MIT's Deep Learning State of the Art lecture referencing this post May 25th update: New graphics (RNN animation, word embedding graph), color coding, elaborated on the final attention example. Note: The animations below are videos. Touch or hover on them (if you're using a mouse) to get play controls so you can pause if needed. Sequence-to-sequence models are deep learning models that have achieved a lot of success in tasks like machine translation, text summarization, and image captioning. Google Translate started using such a model in production in late 2016. These models are explained in the two pioneering papers (Sutskever et al., 2014, Cho et al., 2014)... | |
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www.telesens.co
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| | | | | [LatexPage] Recently, I've been learning about sequence-to-sequence translation systems and going through Pytorch's fairseq code. I've been focusing on the convolutional seq-to-seq method by Gehring et al. The basic idea behind seq-to-seq models is easy to understand, but there are a number of issues in the implementation that I found tricky to understand. In this post, | |
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dennybritz.com
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| | | | | Recurrent Neural Networks (RNNs) are popular models that have shown great promise in manyNLP tasks. | |
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codeincomplete.com
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| | | Personal Website for Jake Gordon | ||
