Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 1 - Introduction and Word Vectors

[@supersnowva6717]

Thanks very much for uploading this Stanford! Very fresh and exciting content! Please keep uploading future videos of this course. Much appreciated!

[@nguyen7272]

amazing!!! thank you guys so much for making these videos , love Stanford ❣

[@stanfordonline]

Thanks for your comment, glad you're enjoying these lectures!

[@rylaczero3740]

I had saved videos two years back but got time to watch just now! so watching the 2019 edition, please post remaining videos episode soon! You have my 10/10 IMDb rating, professor Manning! :)

[@samuraiz2852]

k

[@muditchaturvedi5276]

You are so energized and interesting !

[@francisliubin]

At 1:08:47, the function on the white board, need to change u_o to u_w. Hopefully, this won't make people feel confused.

[@ethanwang2024]

Thanks, Stanford, thx professor manning. Hope one day I could work with you.

[@alinalasskaja631]

Just awesome for me as a polyglot ❤❤

[@NhiNguyễnNgọcYến-i9r]

It's very helpful for me ❤

[@bobonaqa]

He seems like such a nice human being

[@iamjerryliu]

The last 5 mins blows ur mind. 👍

[@iamjerryliu]

That makes one thinking that language and mathematics are just 2 sides of the same coin how we recognize the world and express ourselves.

[@kameshkotwani2714]

dude, just because of you I have to watch the whole video now. you kindled my curiosity.

[@atharvaattarde8778]

Thank you

[@nesa1126]

Thanks

[@black-sci]

43:55 The slide shows 'center word at position t' but immediately next slide 44:00, Wj as center word. Shouldn't Wt be center word.

[@black-sci]

Likelihood is basically negative of loss_function that we use in ml.

[@sajidhaniff01]

Awesome thanks!

[@chuanmingliu5694]

see you again :-)

[@陆恒-g9e]

Thank you, I hope I could have a chance to visit Stanford one day.

[@김창현-v4z]

27:34

[@trisha2596]

What are the prerequisites to this course?

[@stanfordonline]

Hi Trisha, thanks for your question! For the graduate course we require a conferred bachelor’s degree with an undergraduate GPA of 3.0 or better. In addition - you should understand calculus and linear algebra, and one of the following- an introduction to natural language processing (CS124), an introduction to artificial intelligence (CS221), or machine learning (CS229). You can view the graduate course here: online.stanford.edu/courses/cs224n-natural-language-processing-deep-learning For the professional version of this course, you should be proficient in python, have taken college level calculus and linear algebra and understand probability theory. You can view the professional course here: online.stanford.edu/courses/xcs224n-natural-language-processing-deep-learning

[@hermes537]

is this what you learn in MARS? I am a total ignoramus and find this mind-boggling

[@jonathanr4242]

ahhhh... but can an orangutan build a machine that is smarter than an orangutan...

[@giantspacemonstr]

can humans do that? can any carbon based life form do that? just to be clear smarter doesn't mean faster multiplication, it means devising a formula by observing the real world, it means spreading paint on a blank canvas to portray a scene from imagination. people think that machines will eventually surpass human intelligence, but it is very debatable if true consciousness can ever be generated from metals.

[@DigiiFox]

The smartest of orangutans can at least make a machine smarter than the vast majority of other orangutans.

[@jonathanr4242]

@@giantspacemonstr Yes. Totally agree. It's still up for debate. I think LeCun's comment was probably referring to common sense.

[@lunapotter5593]

This thread definitely cleared up what an orangutan is, i was wondering what orangutan is and was too lazy to Google it up😂😂😂

[@John-lf3xf]

A calculator can do all arithmetic faster than almost all people.

[@귄하-m1l]

thanks

[@jiayanguo348]

thanks