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Here [1] is an example of the videos, the player has a handy search feature and links to video parts. update: It would be great to have a way to take your own notes, any chrome extension that can help with that ? [1] https://ox.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=ff9...
around 7 mins 30secs into the introduction
Earlier quoted context omitted.
Language encoding and generation (sequence to sequence).
Could you expand a bit on that. I have come across examples generating poems from Shakespeare works and realistic looking css/javascript but still trying to find out a more realistic usecase.
Here [1] is an example of the videos, the player has a handy search feature and links to video parts. update: It would be great to have a way to take your own notes, any chrome extension that can help with that ? [1] https://ox.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=ff9...
Where could I find a list of links to the videos?
Im taking this course at Oxford and they have been working through the practicals 1-3 (further ones will be posted) For anyone considering working through this outside of Oxford: I think the practicals are the real gems here and should be doable without the practical lab sessions that you get when attending the course. With that being said, they use a dataset a bit closer to a real world assignment. Therefore, it req…
What are the practical uses of language modelling RNNs ? (apart from writing grammar/syntax checkers)
I guess that's a bit indirect, but these RNNs are essentially learning the 'rules' that actual phrases conform to. It'd definitely be better than trying to hard-code the rules (especially for a language like English!). And the training data is very easy to get: just feed it a few thousand ebooks, the comments section from HN etc.
Earlier quoted context omitted.
Could you expand a bit on that. I have come across examples generating poems from Shakespeare works and realistic looking css/javascript but still trying to find out a more realistic usecase.
Say you want to translate a sentence from language A to language B. You have a system that generates 10 possible translations in language B. Now you use a language model of language B to figure out which is the best translation by asking the language model which of the translations has the highest probability of being generated by the model.
Even very simple transformations, e.g., adding alliteration/assonance or adding rhymes everywhere, might be fun.
Im taking this course at Oxford and they have been working through the practicals 1-3 (further ones will be posted) For anyone considering working through this outside of Oxford: I think the practicals are the real gems here and should be doable without the practical lab sessions that you get when attending the course. With that being said, they use a dataset a bit closer to a real world assignment. Therefore, it req…
Regarding practicals: is there anything more than the overview slides ?