uh younes mourri could you give a little
intro to yourself
thanks ryan uh i'm younes and i teach
here at stanford and also deep learning.ai
so you've probably already seen me uh at
deep learning.ai with lucas
in the specialization and i'm also very
interested
in accessible education specifically for
ai
and uh hope to see you all soon
yeah and i just say yeah really grateful
uh to
ken marty and eunice in addition to
lucas for being with us uh
i just want to give a shout out to ken
uh who was a huge help in
uh to to the whole team in creating the
nlp specialization
in addition to being an insightful you
know leading figure
of of nlp so thank you ken and
martin younis i'd like to ask you a
question
you're the the sort of earliest career
panelist here and i think it must be an
interesting perspective to have
been studying all this stuff or sort of
developing your own mastery
while the field was advancing so faster
while it was sort of making such a big
public splash so maybe you could give
some perspective
on on what your journey's been like and
and also any recommendations you'd have
for people who are wanting to
potentially start on that journey
themselves
yeah thanks ryan so my journey has been
a little bit different so for me
education means a lot to me
and especially i grew up in morocco
so we do not have access to the latest
you know technologies and states of the
arts
and as many resources as we have here at
stanford
so for me i really wanted to be part of
the mission that will help you know take
the latest
uh technologies and states of the arts
and
make it accessible to everyone around
the world
so that's how i started and i came here
around in 2014
where i started with the machine
learning course on coursera
and i started with by writing like
lecture notes and trying to make it as
accessible as possible to everyone
and eventually i started building up
and you know studied math computer
science statistics
and just started slowly you know
mastering building assignments
and quizzes and uh so forth
so that was uh pretty much the journey
then we
built the deep learning specialization
and we started teaching uh on stanford
the
campus so it's been interesting because
every time you
uh you finish building an assignment
then there's new technology that
goes out and then you have to read
another paper
and then try to transform it uh into
another assignment and it just keeps
going on and on so uh it's been
definitely exciting to be like you know
as soon as you're done with
uh a new assignment you you go ahead and
try to
uh bring in new uh states of the arts
so yeah i can
definitely say from my own perspective
that working on these courses with you
and lucas has been really interesting in
the fact that the field has
developed significantly while building
the courses so that
that you know feels like it's been an
exciting
uh an exciting path um
andrew i'd like to
cool well um i feel like we could keep
the conversation going for a long time
here but i
we need to save some time at the end
here for for the
course demo with you eunice um so at
this point i want to thank all of the
panelists very much for all the insights
i want to just mention as well that for
people looking to
stay up to date we do publish a weekly
newsletter called the batch
and that we spend a lot of time trying
to figure out what's relevant to think
about this
week and you know sort of bring it down
to a consumable size so that you can try
to stay up to date of course we can only
do so much in there but it's a great
place to catch up on some things
so thank you very much to all
of the panelists and um at this point
eunice i'd like to turn it over to you
um to give us a little demo a little
sneak peek into
uh the course uh that's coming out today
uh yes thank you ryan um let me just
wait perfect i hope everyone can see my
screen now
okay so what i'm about to do right now
i'm gonna give you a course demo of
course three
and uh so why take this specialization
uh the specialization teaches nlp which
is one of the most sought after skills
in ai
and you use nlp every day so text is
everywhere basically
right now you're reading text when you
are looking at
your phone you're using text when you're
sending a message to someone you're
using
autocorrect when you're inputting a
search query it's google search
you're using autocomplete you're
basically using google machine
translation every time
uh you're basically using nlp tools and
applications all the time
so why take specifically the
specialization well the special
the specialization was designed
specifically to
build up to the latest states of the
arts models
so you start with course one and two
where we lay the foundations
and then in courses three and four you
start reaching states of the arts and
hopefully by the end
you will not only know how these apis
work
but also um uh you'll be able to use
them so you'll know how they're built
from scratch and how to use them so who
is this specialization for
uh the first of all the prx for the
specialization are just basic machine
learning
and basic coding so as long as you know
basic machine learning and some basic
coding
uh you'll be able to um
do the specialization software engineers
developers and students of all
backgrounds can easily take the
specialization
and if you're a product manager
entrepreneurs and digital businesses
wants to transform their businesses into
ai powered businesses
then this specialization will also help
you with that
and also people who just want to know
the latest ai trends currently in the
specialization
we do cover the latest uh states of the
arts models
so it will also be a very good thing if
you just wanted to know
what the latest industry
is using so let's take a look at what
you learn
this is just a recap of course one where
you learn to do sentiment analysis with
logistic regression
and naive bayes and then you also learn
about vector space models
uh pca and locality sensitive hashing so
you learn how to translate like one word
from english to french
or you learn how to complete analogies
so this is
course one and it does lay the
foundations then
in course two you learn about dynamic
programming and hidden markov models
so this is where you learn to build
these um
applications like autocorrect
autocomplete
and then parts of speech tagging for
example if someone told you book of lies
then book is a verb
and if someone told you i want to read
the book then book is enough
so this is also useful and has a lot of
consumer enterprise use cases
course three is where we start getting
into more deep learning
and where you cover like dense and
recurrent neural networks
lstms gru's and signing and siamese
networks for example
uh we always answer like sometimes
questions gets answered
uh gets students ask us questions and
the same question
gets you know asked again and again so
with a system like with siamese networks
you can identify whether
a user asked the question that has
already been answered
and you can just recommend that answer
to the user you also learn about text
generation
named entity recognition and how to
identify these question duplicates
and this is course free which uh is
currently states of the arts
where you cover encoder decoder causal
and self attention where you perform the
latest you know state-of-the-art machine
translation
you learn how to take an article and
summarize it uh you do question
answering
and you do chat bots so you're not only
like using an api
what's really special about this course
for is that
you do get to see how these products are
being built from scratch
so you can use them in your own
application and projects
and some of the models covered are t5
birds
transformer reformer reformer is the
efficient transformer so that's what
lucas was talking about
and this allows you to save a lot of
memory
uh and it just makes it much much faster
and allows you to capture
uh long sequences and then we also by
taking course four later you'll also
learn how gpt2 and dpt3 like they're
just bigger models and bigger versions
of themselves
so this is coming soon coming in
september so uh stay tuned for that
but now let's focus on uh course three
which is sequence models so remember
course one was classification vector
spaces
probabilistic models course three
sequence and course four
is the attention models so in week one
of course we are going to do sentiment
analysis and i know you're like oh we've
already seen sentiment analysis
in course one but in course one if you
had
two inputs like i'm happy and i'm sad
this is easy to classify you can do a
logistic regression a naive bayes as
you will see or you've already seen in
course one and you can get it to work
but what if i asked you a question uh
like an input like
with a great setting i almost liked the
movie but things changed when i saw the
end so over here you have great you have
like and then with a naive model you
might
easily classify oh it's a happy a
sentence
but it turns out that it might be a sad
sentence so this is much harder
and this is what you do in the first
programming uh assignments
so over here you have it's much it's
such a nice day think i'll be taking
uh said and ramsgate fish and chips
anyways it's like a positive sentence
and you can see that the model says that
it's positive
but it's it's naive it's simple over
here you have
i hated my day it was the worst i'm so
sad this is obviously a negative
sentence
but over here with the great setting i
almost liked the movie but things
changed when i saw the end
so you can see over here you have the
word like
great and then you have the word like
so but the neural network was capable of
capturing this dependency
and it turns out that it is truly a
negative
uh sentence so you'll be able to capture
you'll basically be able to build
things that will be able to capture
these dependencies
uh week 2 is text generation and
what you'll do is you'll use character
level generation
and use a similar concept in other text
generation applications
so like poems question answering and
chat bots all
make use of text generation uh and this
is
uh an example of character generation so
you can see here
and which down here is 10 bank so it was
generated one character at a time
and then over here uh some more
character generation examples
and all of these are examples that you
will be building
in your programming assignments of week
two of course three
uh week three is named entity
recognition and
this is an example like many french
citizens are going to morocco for
christmas
and you can see i put morocco and french
when french is geopolitical entity
morocco is a geographic entity and
christmas
is a time indicator so you'll be doing
exactly this
in week three and here's a concrete use
case
here's a concrete use case so let's say
that
you want to help businesses with
customer supports tickets by getting
unstructured data from
by getting structured data from
unstructured data so you can use named
entity recognition to help identify
people
places brands monetary values and more
this is an example of a sentence so
peter navarro the white house director
of trade and manufacturing policy of u.s
said in an interview on sunday morning
that
the white house who was working to
prepare for the possibility of a second
wave of the coronavirus in the fall
though he said um it wouldn't be
it wouldn't it wouldn't necessarily come
so you can see peter navarro is a person
white's house is an organization sunday
morning is a time indicator white house
is a
organization and coronavirus fall is a
time
indicator so you'll also be building
this week four
is question duplicates so i'm once again
asking the same question
and you'll be using siamese networks
applied to a specific use case
with the quora data set and you'll use
it to find
similar search queries or similar
sentences and this is a very important
application because
you do not want to be answering the same
question again and again
so this is an example of uh the
programming assignment towards the end
and of course over here we're giving you
like the built version but you're going
to build this firm
you're going to have to code it so
the question one says do they enjoy
eating the dessert
and question two do they like hiking in
the desert
so you can see that these are not the
same right so it's false
uh next if you were to change hiking
uh in with do do they like the
dessert uh the deserts and do they enjoy
eating
uh the dessert then this
will also be false now i'm going to add
an s here
and you're going to see that these are
still not the same because
i can enjoy for example eating something
uh
i can like something like kale because
it's healthy but i might not be
i might not enjoy eating kale uh so it's
also false
but if you were to change it like this
then you get it's true do they enjoy
eating the desserts and do they like
eating the desserts then it gives you
true
so this is just an example of
uh week four and the programming
assignments that you will be building
and in conclusion so we designed
the specialization to not only show you
how to use apis but
also how to build them from scratch and
most assignments could be turned into
products for consumer enterprise
and assignments are strategically
designed to build up to the states of
the art
like gpt3 which is currently states of
the arts
and you just now have the tools and all
the necessary
equipment to start building any products
that you want hopefully turn your ideas
into a fully fleshed out product and
