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Comment by jkbschwarz | Hacker News Reader

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 requires some patience when wrangling the data especially for the later practicals.

However, the patience should pay off and it is rewarding once you build your own nonsense spewing TEDbot!

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Would you be able to post your own solutions for the non sense spewing ted bot? I'd like to jump into hacking on something that already works while also working through the theory from the lectures

Yes happy to share once the evaluation period is over, but sadly I dont think Im allowed to share beforehand. Will have to check when but I expect it should be in approximately 2 weeks.

In the meantime, you might want to check out this excellent blog post http://r2rt.com/recurrent-neural-networks-in-tensorflow-ii.h.... This will provide you with skeleton code to implement a character level generative model (similar to Practical 3, Task 2 with the exception that there you will generate words and not characters). Andrej Karpathy's blog post on LSTMs is also excellent and I believe he also provides the code in his repository as well http://karpathy.github.io/2015/05/21/rnn-effectiveness/

Regarding practicals: is there anything more than the overview slides ?

yes if you go to the root https://github.com/oxford-cs-deepnlp-2017/ you will find the practicals 1-3. Practical 1 has a jupyter notebook skeleton. Practicals 2 & 3 are simply questions guiding your implementation, but you are supposed to write it yourself from scratch.

The org : https://github.com/oxford-cs-deepnlp-2017

That contains repos for practicals, eg. Practical 1: https://github.com/oxford-cs-deepnlp-2017/practical1

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