Machine Learning over Coffee with Josh Gordon - Coffee with a Googler Video Lecture | Coffee with a Googler - IT & Software

today I'm in the Big Apple meeting with
Josh Gordon from Google to talk about
machine learning where we will dig into
how it works why it's important and
where you can learn all about it
welcome to coffee with a Googler in New
York City I'm Laurence Moroney and I'm
here today speaking with Joshua Gordon
now it's something that a lot of people
don't really understand what machine
learning is in a concrete manner so
learn so machine learning is all about
learning from examples rather than
writing manual rules got it
so regular the short way of saying that
it's regular programming is you write a
lot of manual rules to solve a problem
and a machine learning you let the
algorithm find those rules for you got
it from examples some pattern matching
it might be visual or it might be like
other patterns that are hidden in
absolutely and so the input to machine
so the beauty of machine learning like
the real secret sauce is that an
algorithm that learns patterns from data
can solve thousands of different
problems and the reason is if I write a
Python program to recognize digits
my program is hard-coded to work with
digits got it but if I write an
algorithm to learn patterns from data
right I can use that for speech
recognition image recognition medicine
basically anything that you can start
with examples just tell apart a and B my
same algorithm that I wrote just once
where I can tackle all these problems
and that's a really special and actually
fairly profound thing absolutely yeah
now one of the things in your class is
that you're talking about that you're
starting with language right you're
starting with Java and Python I think it
was these yes absolutely so how is the
class going to be structured for people
who want to be these data scientists
absolutely so first of all there are
zero prerequisites
let's not sure there's one pre-race
favorite yeah okay well what's the one
prereqs a sick programming ability okay
in Java or Python ok and by basic I mean
like you can you can run scripts and you
can tweak them okay that's it go um a
little bit of high school math and that
means like basic algebra okay a sick
geometry when I say basic geometry like
to be totally honest if you ask me like
what's sine and cosine like I would
happen to Google it I don't remember
honestly so just basic familiarity okay
and that's it and we're gonna teach the
class in three ways we're going to teach
it totally from the ground up so one
problem I had with these with some of
the academic classes I took is that
they'll talk about a fancy
like neural networks but they'll talk
about in terms of math and so at the end
of the class I don't know how to build
that I can't really do it we're doing it
in Reverse way we're building it step by
step and we're explaining only the math
that's really necessary as we go okay
and instead of equations we're gonna use
visual examples perfect so an equation
could be like if you talked about
gradient descent gradient descent
basically means finding the minimum of a
function mm-hmm so if I just say that
like it's developer I'm like all right
like what does that mean so you can
think of like any equation like X cubed
plus y squared plus whatever equals 7
yep there's some value of x and y that's
going to be the bottom of that curve or
not equal set like equals some value
yeah right yeah anyway you can find the
bottom of that curve but literally by
thinking as a bowl you can like drop
drop a pizza like fruit in a bowl and it
will roll to the bottom yeah and like
gradient descent just means finding like
where this function is zero and like you
can actually describe that really simply
like only like ten or twelve lines of
Python actually sure instead of like
five slides of equations and I think
it's also important to understand why
you need to find the bottom of the curve
and neck absolutely I just focus on that
example absolutely so that's difficult
to describe concisely right so
oftentimes anytime so in machine
learning let's say you're writing an
algorithm to let's say it's to
distinguish apples from oranges all
right you always want to know how
accurate is my algorithm right like I
can solve that problem in one line I can
just say return you know math dot random
and then so like one line mascot right
yeah that would be the perfect one my
accuracy is crap
50% right yeah right between an apple
it's a one-liner yeah right but really
we want to we want to get another way of
describing accuracies you can think
about in terms of air mmm like high
accuracy means low air okay and you can
you can have an equation that describes
your air and the minimum of that
equation is going to give you the
highest accuracy so you can write your
machine learning algorithm to try and
minimize the equation that describes the
air got it and like we'll make that
super concrete in the class but that's
that's where minimization comes in and
that's where gradient descent comes in
so one of the things you're saying in
the class you're teaching just a pure
java python version but there's also a
version where you're bringing in
preexisting libraries that have come
from academia absolutely solve a lot of
this for you absolutely so I want to do
a couple things one is I want to provide
the TLDR
okay so like honestly like as a
developer I like to get up and running
really fast so we're also going to use
open source libraries from just
different universities there's one in
New Zealand that I really love we're
going to show you how to build so
basically first everything from the
ground up step by step from scratch and
the reason we do that is because it
keeps us honest yep if you build every
single piece you have a some
understanding of every single piece and
if you're relying on somebody else
having done the work you don't fully get
to understand it exactly now another
thing is using the open source libraries
honestly you can solve probably 80 or
90% of the machine learning problems you
would like as a data scientist yes now
when you get to the really gigantic
problems then really it makes sense to
use the cloud so we're also going to
teach how to solve problems using Google
API s but that's at the very end of the
class and it's totally optional it's if
this is all on YouTube all on YouTube
there might be some ads on it but that
that's literally it
we think it's gonna be awesome like
source code and stuff that you've done
your source code beyond get up that's
all I'll be okay perfect and the reason
I was hesitating is I'm writing all this
as we're speaking it so like I'm totally
exhausted but yes totally 1% out there
you still looking energetic to me some
I've had a lot of coffee with a Googler
yeah good so like well yeah iPhone I'm
really looking forward to this course
I'm looking forward to you know learning
what you have to teach I've had the same
kind of struggles as you when trying to
understand you know the math behind this
and why I'm doing the math which is why
I had those pointed questions earlier
absolutely
so thanks Josh that was a whole lot of
fun and I've learned so much about
machine learning just just from these
three minutes with you so I'm really
looking forward to your class thank so
much if you've enjoyed this episode of
coffee with a Googler and if you want to
learn machine learning for yourself if
you have any questions for Joshua if you
have any questions for me please leave
them in the comments below and tune into
the Google developers channel for more
great videos including episodes of
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