
An Introduction to Deep Learning for Language Modeling - oDot
http://ofir.io/Neural-Language-Modeling-From-Scratch?a=1
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make3
these lectures at Oxford on the subject (by some research scientist it
deepmind, no less) are some of the best spent time in my career

[https://github.com/oxford-cs-
deepnlp-2017/lectures](https://github.com/oxford-cs-deepnlp-2017/lectures)

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p1esk
Are they better than 224d at Stanford?

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make3
definitely more advanced. they don't cover neural nets 101 like cs224d does,
so they have more time to do advanced subjects like neural machine
translation, neural Turing machines and question answering in the last
courses.

honestly i would just watch both, and start with cs224d, but if I would have
to pick one, it would be Oxford's.

~~~
woodson
Also, check out Graham Neubig's NN for NLP class at CMU:
[http://phontron.com/class/nn4nlp2017/schedule.html](http://phontron.com/class/nn4nlp2017/schedule.html)
It covers a lot of current stuff. The class is still running, so not all
videos/materials are there yet.

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brian_spiering
Here is my listicle about Deep Learning for NLP
[https://github.com/brianspiering/awesome-
dl4nlp](https://github.com/brianspiering/awesome-dl4nlp)

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peterburkimsher
I'm making a Chinese-English parallel translator.

[https://pingtype.github.io](https://pingtype.github.io)

My boss asked me to take CS231n about Deep Learning, and I decided to put some
of my Chinese data into the same tools (Word2Vec) to see how it performed.

Honestly, it sucked. Was that my bad coding (very possible). Or is it because
the examples are cherry-picked to work well with English? Can someone
recommend a tutorial of Deep Learning for languages that are not English?

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wilde
Try stuff from Baidu. They claim good performance in Chinese.

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mozumder
Has language modeling achieved something that can pass a Turing test yet for
various audiences?

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dmreedy
Simply, no.

Most of big advances in the past five years have been around Natural Language
'Parsing' of unstructured information into structured information (this is
what Deep Learning is _very_ good at, so far at least); classifying
unstructured utterances into structured intents, mapping unstructured
utterance 'questions' to unstructured answers in a corpus (fundamentally
similar search), literal parsing of utterances to shallow or deep (semantic)
parses, and so on. The thoroughly end-to-end, multi-modal and multi-scoped
task of participating in a conversation remains largely unsolved. As any non-
primitive interaction with Siri or Alexa should indicate. Word of the day is
"brittle".

