
Natural Language Processing with Deep Learning - jonbaer
http://web.stanford.edu/class/cs224n/
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jimmy_dean
The machine learning Stanford courses are probably the best open education
contributions I've encountered. From Ng's CS229 material to Karpathy's
rendition of CS231n. These are some of the best pedagogical materials on
machine learning/deep learning available.

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discordance
Unfortunately the videos for this course won't be made available until after
the course has finished.

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_cs2017_
[https://youtu.be/8rXD5-xhemo](https://youtu.be/8rXD5-xhemo)

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mark_l_watson
Thanks for the link, just subscribed.

Years ago I took Chris Manning and Dan Jurafsky‘s Coursera NLP class and it
was excellent. I also own, I think, every book they have written including
Jurafsky’s book on food.

It is extremely generous of top universities to make their classes available
online. Of course, watching the videos and trying the homework assignments on
one’s own is not the experience of going to Stanford, but it is less
expensive!

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theCricketer
I took this class and can vouch for it. They update the class every year to go
over recent research - not an easy task in such a fast moving field. For
example, this offering covers the Transformer architecture which has recently
been used to obtain state of the art results across a wide range of NLP tasks.

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roadbeats
Are you a student at Stanford ? I was wondering if I can join just for this
course...

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theCricketer
The videos from the winter 2017 offering are freely available on YouTube.

[https://www.youtube.com/playlist?list=PLqdrfNEc5QnuV9RwUAhoJ...](https://www.youtube.com/playlist?list=PLqdrfNEc5QnuV9RwUAhoJcoQvu4Q46Lja)

The assignments are at the class webpage too.

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ivan_ah
And this is the 2019 playlist:
[https://www.youtube.com/watch?v=8rXD5-xhemo&list=PLoROMvodv4...](https://www.youtube.com/watch?v=8rXD5-xhemo&list=PLoROMvodv4rOhcuXMZkNm7j3fVwBBY42z)

Quote from course page: "This year, CS224n will be taught for the first time
using PyTorch rather than TensorFlow (as in previous years)."

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godelmachine
Is there a good course for standalone Natural Language Processing?

I came across MIT OCW’s Advanced NLP -
[https://ocw.mit.edu/courses/electrical-engineering-and-
compu...](https://ocw.mit.edu/courses/electrical-engineering-and-computer-
science/6-864-advanced-natural-language-processing-fall-2005/) , but does
someone has a similar or a better standalone NLP course in mind? From MIT OCW,
Stanford , Coursera, Udacity, et al?

I am very much interested. Could be free or paid.

Even PDF’s of lecture notes could do.

Edit - Found one standalone NLP course by CMU, looks good -
[http://demo.clab.cs.cmu.edu/NLP/](http://demo.clab.cs.cmu.edu/NLP/)

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alok-g
The following is also good:

[https://web.stanford.edu/class/cs224u/](https://web.stanford.edu/class/cs224u/)

While you asked for a course, checkout my introductory presentation on the
subject here (PDF slides available for download):

[https://www.siliconvalley-
codecamp.com/Session/2014/introduc...](https://www.siliconvalley-
codecamp.com/Session/2014/introduction-to-natural-language-processing-nlp)

An attendee, claiming to have read 1.5 books on NLP, called it the broadest
coverage of NLP he had ever seen. Some of the attendees told me that I had
inspired them to turn their careers to NLP.

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godelmachine
I skimmed through your PDF. It's very exhaustive and all-encompassing.

But what really got me excited was that you had included 2 of Steven Pinker's
books in your "Suggested References". Few weeks ago, I was going over the
Wikipedia entry this book by Steven Pinker - "A Sense of Style", and wondering
if it has any implications on linguistics.

[https://en.wikipedia.org/wiki/The_Sense_of_Style](https://en.wikipedia.org/wiki/The_Sense_of_Style)

So, my question to you is, does "The Sense of Style" bears any NLP-relevant
context in it?

Thanks for such an awesome PDF :) Can't wait for my day to end!

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alok-g
Glad that you like it so far. :-)

I wish I had recorded the session I gave. It was several times more
interesting and engaging than what the slides read on their own.

I haven't read this book, but have read a blog by Pinker on the same lines. I
just also skimmed through the Wikipedia link you mentioned.

The book would not be a recommendation from NLP algorithms perspective. I am
sure though that it would be a good book as Pinker is a mind-opening writer.

I'll nevertheless give you some deep food for thought relating to languages:

Check out slide 14 in my talk, especially the remark on its right side. It is
a powerful thought.

There is an equilibrium process involved in the shaping of the language over
the time, though language must by definition have at least some
standardization for it to work, which means it would resist change.

What Pinker is saying at the high level is that the official rules of grammar
sometimes deviate away from the equilibrium point. For example, technical
jargon and acronyms are often easier for the speaker than the listener. A poor
handwriting is likely less tiring for the writer than easier for the reader.

There would also be deviations which make it harder for _both_ the speaker and
the listener.

Yet, in both of the cases above, the language would resist change.

As writing came into being (keep in mind that we have been talking for a few
million years, and writing only for a few thousand, so no comparison!),
written material stands for much longer than vibrations in air molecules, or
our memories, do. That further slows down language velocity as standardization
now sits across time too.

We now stand where language is standardizing across the world, and getting
frozen in the Internet, there's further velocity reduction involved. (Albeit,
newer concepts are adding increasingly faster to the languages...)

How do you, in such cases, make the above deviations from the optimum go away?

There are some rules of language, grammar, that shouldn't be. They are like
legacy code.

Pinker is educating us about such rules. He's trying to make style come back
to that optimal.

Let's take a simple example. Did you know, comma classically sits inside
quotes? Here's an example I picked from [1]:

"Good morning, Frank," said Hal.

Note that the comma after Frank is inside the quotes! Why should it be that
way? No wonder putting comma outside is gaining acceptance. :-)

There are even weird rules for what happens when multiple paragraphs are to be
included inside a single quote. (Hints: Number of opening and closing quotes
is not equal in some English dialects under this scenario. Weird, hmmm.)

These rules should just go away. It's better to choose the optimals for the
language and make those cultural, style, shifts happen in our language.

Take care. Good discussion. Feel free to reach out again.

And please feel free to refer others to my slides page as you see fit. :-)

[1]
[https://en.m.wikipedia.org/wiki/Quotation_marks_in_English](https://en.m.wikipedia.org/wiki/Quotation_marks_in_English)

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godelmachine
Wow, the info you have provided is so much valuable.

What you said above definitely sounds like Steven Pinker-ish.

I feel like I am embarking on a learning process that’s gonna stay with me for
a long time, courtesy our short correspondence.

I know I will be needing you again, so I am going to save your email ID in my
contacts. I will send you a Hi on your email ID , if you don’t mind?

And I am recommending your PDF to my batchmates who have enrolled for the same
course :)

Muchas gracias for the wonderful explaination!

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alok-g
Sure! alokgovil hot mail :-)

Also, feel free to connect on LinkedIn: /in/alokgovil.

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piceas
Skimming through I came across a fun example (new to me at least) in the
introduction to translation, lecture 8 slides page 55.

"hn hn hn hn hn hn hn hn hn hn hn hn hn hn hn hn hn hn hn" in Somali gives
"Serious Therapic Anxiety syndrome" on google translate.

Perhaps not surprising, but fun. The link from the slide (below) has
previously been submitted to hn without notable discussion.

[https://www.skynettoday.com/briefs/google-nmt-
prophecies](https://www.skynettoday.com/briefs/google-nmt-prophecies)

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k4ch0w
Chris Manning is responsible for so much work in this field. I can't wait to
go through this video series. Thanks for all your hard work and I want you to
know how much it's appreciated.

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return0
I also like CMU's neural net for nlp class:
[https://www.youtube.com/watch?v=pmcXgNTuHnk](https://www.youtube.com/watch?v=pmcXgNTuHnk)

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_cs2017_
Great lectures, with a lot of intuition about recent research and clear
explanations.

Some videos are already online, starting with
[https://youtu.be/8rXD5-xhemo](https://youtu.be/8rXD5-xhemo)

