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I'm<00:00:12.990><c> Carlos</c><00:00:13.469><c> gastrin</c><00:00:14.040><c> I'm</c><00:00:14.250><c> the</c><00:00:14.580><c> Amazon</c>

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I'm Carlos gastrin I'm the Amazon
 

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I'm Carlos gastrin I'm the Amazon
professor<00:00:15.389><c> machine</c><00:00:15.719><c> learning</c><00:00:16.049><c> at</c><00:00:16.139><c> the</c>

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professor machine learning at the
 

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professor machine learning at the
University<00:00:16.710><c> of</c><00:00:16.770><c> Washington</c><00:00:17.279><c> and</c><00:00:17.400><c> I'm</c><00:00:17.640><c> the</c><00:00:17.820><c> CEO</c>

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University of Washington and I'm the CEO
 

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University of Washington and I'm the CEO
of<00:00:18.330><c> data</c><00:00:25.580><c> we've</c><00:00:26.580><c> reached</c><00:00:26.789><c> a</c><00:00:26.939><c> phase</c><00:00:27.180><c> transition</c>

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of data we've reached a phase transition
 

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of data we've reached a phase transition
in<00:00:28.170><c> the</c><00:00:28.320><c> world</c><00:00:28.560><c> really</c><00:00:28.769><c> in</c><00:00:28.920><c> the</c><00:00:29.010><c> world</c><00:00:29.160><c> of</c>

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in the world really in the world of
 

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in the world really in the world of
machine<00:00:29.550><c> learning</c><00:00:29.580><c> what</c><00:00:30.090><c> we're</c><00:00:30.210><c> going</c><00:00:30.449><c> from</c>

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machine learning what we're going from
 

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machine learning what we're going from
just<00:00:31.500><c> an</c><00:00:31.590><c> academic</c><00:00:31.830><c> endeavor</c><00:00:32.520><c> to</c><00:00:32.700><c> the</c><00:00:33.600><c> real</c>

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just an academic endeavor to the real
 

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just an academic endeavor to the real
world<00:00:33.870><c> being</c><00:00:34.380><c> impacted</c><00:00:34.620><c> by</c><00:00:34.890><c> intelligent</c>

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world being impacted by intelligent
 

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world being impacted by intelligent
applications<00:00:35.940><c> they</c><00:00:36.090><c> use</c><00:00:36.239><c> machine</c><00:00:36.570><c> learning</c>

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applications they use machine learning
 

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applications they use machine learning
at<00:00:37.020><c> their</c><00:00:37.170><c> core</c><00:00:37.530><c> and</c><00:00:37.770><c> in</c><00:00:38.640><c> an</c><00:00:38.790><c> event</c><00:00:39.030><c> like</c><00:00:39.090><c> this</c>

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at their core and in an event like this
 

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at their core and in an event like this
you<00:00:39.600><c> see</c><00:00:39.629><c> developers</c><00:00:40.469><c> and</c><00:00:40.710><c> later</c><00:00:40.829><c> scientists</c>

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you see developers and later scientists
 

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you see developers and later scientists
coming<00:00:41.640><c> together</c><00:00:41.760><c> with</c><00:00:42.510><c> the</c><00:00:42.719><c> goal</c><00:00:42.960><c> of</c>

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coming together with the goal of
 

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coming together with the goal of
building<00:00:43.829><c> such</c><00:00:44.219><c> intelligent</c><00:00:44.789><c> applications</c>

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the<00:00:52.680><c> timing</c><00:00:53.129><c> for</c><00:00:53.160><c> Billy</c><00:00:53.489><c> intelligence</c>

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the timing for Billy intelligence
 

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the timing for Billy intelligence
applications<00:00:54.090><c> is</c><00:00:54.600><c> really</c><00:00:55.140><c> right</c><00:00:55.469><c> right</c><00:00:55.980><c> now</c>

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applications is really right right now
 

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applications is really right right now
so<00:00:56.670><c> companies</c><00:00:57.059><c> have</c><00:00:57.300><c> invested</c><00:00:57.420><c> time</c><00:00:58.289><c> and</c>

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so companies have invested time and
 

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so companies have invested time and
energy<00:00:58.980><c> collecting</c><00:00:59.969><c> data</c><00:01:00.090><c> putting</c><00:01:00.660><c> one</c><00:01:00.840><c> to</c>

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energy collecting data putting one to
 

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energy collecting data putting one to
one<00:01:01.379><c> place</c><00:01:01.559><c> we're</c><00:01:01.800><c> not</c><00:01:01.920><c> getting</c><00:01:02.129><c> enough</c><00:01:02.340><c> value</c>

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one place we're not getting enough value
 

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one place we're not getting enough value
out<00:01:02.789><c> of</c><00:01:02.820><c> it</c><00:01:03.030><c> on</c><00:01:03.870><c> the</c><00:01:04.019><c> other</c><00:01:04.049><c> hand</c><00:01:04.260><c> the</c>

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out of it on the other hand the
 

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out of it on the other hand the
company's<00:01:04.739><c> herb</c><00:01:04.830><c> garden</c><00:01:05.159><c> value</c><00:01:05.460><c> have</c>

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company's herb garden value have
 

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company's herb garden value have
differentiated<00:01:06.750><c> their</c><00:01:07.170><c> offerings</c><00:01:07.890><c> by</c><00:01:08.400><c> doing</c>

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differentiated their offerings by doing
 

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differentiated their offerings by doing
something<00:01:08.909><c> exciting</c><00:01:09.119><c> their</c><00:01:09.450><c> data</c><00:01:09.659><c> Abby</c><00:01:10.560><c> it</c>

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something exciting their data Abby it
 

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something exciting their data Abby it
amazon<00:01:11.610><c> in</c><00:01:11.760><c> the</c><00:01:11.790><c> early</c><00:01:12.210><c> days</c><00:01:12.390><c> with</c>

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amazon in the early days with
 

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amazon in the early days with
recommender<00:01:13.290><c> systems</c><00:01:13.740><c> or</c><00:01:14.010><c> uber</c><00:01:14.790><c> today</c><00:01:15.090><c> of</c><00:01:15.299><c> how</c>

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recommender systems or uber today of how
 

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recommender systems or uber today of how
we<00:01:16.560><c> know</c><00:01:16.680><c> did</c><00:01:16.890><c> their</c><00:01:17.040><c> self</c><00:01:17.250><c> in</c><00:01:17.340><c> the</c><00:01:17.369><c> taxi</c>

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we know did their self in the taxi
 

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we know did their self in the taxi
industry<00:01:17.850><c> and</c><00:01:18.360><c> so</c><00:01:18.720><c> the</c><00:01:18.840><c> timing</c><00:01:19.080><c> is</c><00:01:19.320><c> just</c><00:01:19.350><c> right</c>

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industry and so the timing is just right
 

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industry and so the timing is just right
to<00:01:20.130><c> do</c><00:01:20.310><c> that</c><00:01:20.340><c> and</c><00:01:20.869><c> the</c><00:01:21.869><c> creativity</c><00:01:22.320><c> now</c><00:01:22.799><c> can</c><00:01:22.979><c> be</c>

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to do that and the creativity now can be
 

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to do that and the creativity now can be
unleashed<00:01:23.460><c> to</c><00:01:23.759><c> do</c><00:01:23.909><c> things</c><00:01:24.180><c> we</c><00:01:24.330><c> never</c><00:01:24.540><c> imagined</c>

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unleashed to do things we never imagined
 

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unleashed to do things we never imagined
could<00:01:25.229><c> be</c><00:01:25.320><c> done</c><00:01:25.500><c> before</c><00:01:25.830><c> with</c><00:01:26.040><c> data</c>

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there<00:01:34.780><c> is</c><00:01:34.870><c> a</c><00:01:34.900><c> journey</c><00:01:35.310><c> to</c><00:01:36.310><c> create</c><00:01:36.580><c> intelligent</c>

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there is a journey to create intelligent
 

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there is a journey to create intelligent
applications<00:01:37.780><c> that</c><00:01:37.990><c> journey</c><00:01:38.260><c> starts</c><00:01:38.920><c> from</c>

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applications that journey starts from
 

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applications that journey starts from
being<00:01:39.610><c> inspired</c><00:01:40.210><c> by</c><00:01:40.780><c> the</c><00:01:41.710><c> possibility</c><00:01:42.490><c> of</c>

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being inspired by the possibility of
 

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being inspired by the possibility of
what<00:01:43.120><c> you</c><00:01:43.240><c> can</c><00:01:43.420><c> do</c><00:01:43.660><c> and</c><00:01:43.900><c> how</c><00:01:44.080><c> could</c><00:01:44.260><c> how</c><00:01:44.680><c> can</c>

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what you can do and how could how can
 

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what you can do and how could how can
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you do that easily and quickly all the
 

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you do that easily and quickly all the
way<00:01:47.230><c> to</c><00:01:47.290><c> take</c><00:01:47.620><c> it</c><00:01:47.770><c> into</c><00:01:47.890><c> the</c><00:01:48.010><c> production</c><00:01:48.190><c> to</c>

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way to take it into the production to
 

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way to take it into the production to
impact<00:01:49.030><c> your</c><00:01:49.270><c> users</c><00:01:49.780><c> or</c><00:01:49.960><c> your</c><00:01:50.080><c> customers</c><00:01:50.830><c> and</c>

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impact your users or your customers and
 

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impact your users or your customers and
so<00:01:51.550><c> there</c><00:01:51.820><c> are</c><00:01:51.850><c> two</c><00:01:52.120><c> things</c><00:01:52.150><c> that</c><00:01:52.540><c> I</c><00:01:53.110><c> talked</c>

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so there are two things that I talked
 

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so there are two things that I talked
about<00:01:53.650><c> today</c><00:01:53.800><c> I'm</c><00:01:54.160><c> super</c><00:01:54.490><c> excited</c><00:01:54.580><c> about</c><00:01:54.880><c> one</c>

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about today I'm super excited about one
 

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about today I'm super excited about one
is<00:01:55.810><c> ways</c><00:01:56.080><c> to</c><00:01:56.290><c> accelerate</c><00:01:56.530><c> that</c><00:01:56.920><c> journey</c><00:01:57.190><c> to</c>

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is ways to accelerate that journey to
 

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is ways to accelerate that journey to
help<00:01:58.030><c> say</c><00:01:58.540><c> developers</c><00:01:59.170><c> they</c><00:01:59.320><c> don't</c><00:01:59.500><c> have</c><00:01:59.620><c> a</c>

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help say developers they don't have a
 

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help say developers they don't have a
motion<00:01:59.830><c> learning</c><00:02:00.490><c> background</c><00:02:00.880><c> to</c><00:02:01.330><c> be</c><00:02:01.450><c> able</c><00:02:01.540><c> to</c>

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motion learning background to be able to
 

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motion learning background to be able to
do<00:02:01.840><c> that</c><00:02:01.990><c> easily</c><00:02:02.260><c> and</c><00:02:02.590><c> quickly</c><00:02:02.740><c> using</c><00:02:03.340><c> the</c>

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do that easily and quickly using the
 

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do that easily and quickly using the
data<00:02:03.760><c> platform</c><00:02:04.300><c> and</c><00:02:04.540><c> the</c><00:02:05.350><c> second</c><00:02:05.830><c> thing</c><00:02:05.860><c> is</c>

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data platform and the second thing is
 

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data platform and the second thing is
the<00:02:06.190><c> question</c><00:02:06.520><c> of</c><00:02:06.610><c> trust</c><00:02:07.420><c> how</c><00:02:07.870><c> can</c><00:02:08.050><c> you</c><00:02:08.080><c> know</c>

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the question of trust how can you know
 

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the question of trust how can you know
when<00:02:08.950><c> to</c><00:02:09.130><c> trust</c><00:02:09.460><c> a</c><00:02:09.610><c> machine</c><00:02:09.940><c> learning</c><00:02:10.300><c> model</c>

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when to trust a machine learning model
 

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when to trust a machine learning model
in<00:02:10.869><c> production</c><00:02:11.440><c> in</c><00:02:11.950><c> other</c><00:02:12.100><c> words</c><00:02:12.370><c> if</c><00:02:12.550><c> I</c><00:02:13.030><c> have</c>

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in production in other words if I have
 

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in production in other words if I have
an<00:02:13.300><c> idea</c><00:02:13.420><c> it</c><00:02:13.870><c> seems</c><00:02:13.930><c> to</c><00:02:14.320><c> work</c><00:02:14.530><c> how</c><00:02:14.800><c> do</c><00:02:14.860><c> I</c><00:02:14.950><c> know</c>

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an idea it seems to work how do I know
 

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an idea it seems to work how do I know
that<00:02:15.220><c> it's</c><00:02:15.340><c> working</c><00:02:15.610><c> how</c><00:02:15.940><c> do</c><00:02:16.000><c> I</c><00:02:16.060><c> know</c><00:02:16.120><c> it's</c>

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that it's working how do I know it's
 

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that it's working how do I know it's
working<00:02:16.660><c> for</c><00:02:16.810><c> the</c><00:02:16.870><c> right</c><00:02:17.050><c> reasons</c><00:02:17.560><c> and</c><00:02:17.709><c> how</c>

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working for the right reasons and how
 

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working for the right reasons and how
can<00:02:18.430><c> I</c><00:02:19.080><c> keep</c><00:02:20.080><c> doing</c><00:02:20.560><c> that</c><00:02:20.680><c> as</c><00:02:21.010><c> I</c><00:02:21.250><c> take</c><00:02:21.400><c> the</c>

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can I keep doing that as I take the
 

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can I keep doing that as I take the
production<00:02:22.030><c> so</c><00:02:22.420><c> that</c><00:02:22.450><c> i</c><00:02:22.600><c> know</c><00:02:22.660><c> that</c><00:02:22.959><c> if</c><00:02:23.050><c> it's</c>

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production so that i know that if it's
 

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production so that i know that if it's
still<00:02:23.560><c> working</c><00:02:23.620><c> still</c><00:02:24.550><c> doing</c><00:02:24.820><c> what</c><00:02:24.970><c> expected</c>

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still working still doing what expected
 

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still working still doing what expected
to<00:02:25.570><c> do</c><00:02:25.750><c> so</c><00:02:26.260><c> if</c><00:02:26.470><c> the</c><00:02:26.560><c> data</c><00:02:26.740><c> platform</c><00:02:27.220><c> we're</c>

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to do so if the data platform we're
 

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to do so if the data platform we're
thinking<00:02:27.700><c> both</c><00:02:27.910><c> about</c><00:02:28.270><c> accelerating</c><00:02:28.900><c> the</c>

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thinking both about accelerating the
 

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thinking both about accelerating the
journey<00:02:29.290><c> but</c><00:02:29.770><c> also</c><00:02:30.010><c> providing</c><00:02:30.610><c> transparency</c>

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journey but also providing transparency
 

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journey but also providing transparency
and<00:02:31.600><c> clarity</c><00:02:31.870><c> for</c><00:02:32.470><c> machine</c><00:02:32.770><c> learning</c><00:02:32.800><c> models</c>

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so<00:02:43.920><c> I've</c><00:02:44.760><c> been</c><00:02:44.940><c> teaching</c><00:02:45.060><c> machine</c><00:02:45.480><c> learning</c>

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so I've been teaching machine learning
 

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so I've been teaching machine learning
for<00:02:45.870><c> a</c><00:02:45.900><c> long</c><00:02:46.110><c> time</c><00:02:46.410><c> and</c><00:02:46.709><c> I</c><00:02:46.770><c> have</c><00:02:46.920><c> lots</c><00:02:47.400><c> of</c><00:02:47.610><c> ideas</c>

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for a long time and I have lots of ideas
 

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for a long time and I have lots of ideas
of<00:02:48.240><c> how</c><00:02:48.390><c> that</c><00:02:48.450><c> can</c><00:02:48.780><c> be</c><00:02:48.930><c> done</c><00:02:49.080><c> but</c><00:02:49.350><c> today</c><00:02:49.709><c> the</c>

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of how that can be done but today the
 

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of how that can be done but today the
way<00:02:50.040><c> I</c><00:02:50.069><c> think</c><00:02:50.400><c> about</c><00:02:50.610><c> it</c><00:02:50.730><c> is</c><00:02:50.940><c> start</c><00:02:51.690><c> from</c><00:02:51.810><c> the</c>

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way I think about it is start from the
 

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way I think about it is start from the
use<00:02:52.080><c> cases</c><00:02:52.350><c> start</c><00:02:53.160><c> from</c><00:02:53.370><c> the</c><00:02:53.510><c> applications</c>

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use cases start from the applications
 

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use cases start from the applications
open<00:02:55.380><c> them</c><00:02:55.770><c> up</c><00:02:55.920><c> because</c><00:02:56.160><c> those</c><00:02:56.489><c> can</c><00:02:56.700><c> be</c><00:02:56.730><c> really</c>

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open them up because those can be really
 

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open them up because those can be really
inspired<00:02:57.330><c> and</c><00:02:57.750><c> try</c><00:02:58.590><c> to</c><00:02:58.650><c> figure</c><00:02:58.860><c> out</c><00:02:59.040><c> how</c><00:02:59.370><c> those</c>

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inspired and try to figure out how those
 

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inspired and try to figure out how those
were<00:02:59.670><c> built</c><00:03:00.110><c> and</c><00:03:01.110><c> were</c><00:03:01.410><c> the</c><00:03:01.560><c> techniques</c><00:03:01.950><c> are</c>

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were built and were the techniques are
 

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were built and were the techniques are
support<00:03:02.520><c> them</c><00:03:02.730><c> and</c><00:03:02.940><c> from</c><00:03:03.510><c> that</c><00:03:03.690><c> you</c><00:03:03.750><c> can</c>

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support them and from that you can
 

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support them and from that you can
prioritize<00:03:04.590><c> what</c><00:03:05.160><c> the</c><00:03:05.250><c> things</c><00:03:05.430><c> you</c><00:03:05.550><c> can</c><00:03:05.700><c> learn</c>

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prioritize what the things you can learn
 

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prioritize what the things you can learn
and<00:03:06.209><c> how</c><00:03:06.630><c> so</c><00:03:07.260><c> with</c><00:03:08.069><c> emily</c><00:03:08.790><c> fox</c><00:03:09.000><c> university</c><00:03:09.480><c> of</c>

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and how so with emily fox university of
 

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and how so with emily fox university of
washington<00:03:10.410><c> we've</c><00:03:10.620><c> created</c><00:03:10.860><c> a</c><00:03:11.130><c> Coursera</c>

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washington we've created a Coursera
 

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washington we've created a Coursera
specialization<00:03:13.320><c> machine</c><00:03:13.560><c> learning</c><00:03:13.950><c> that</c>

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specialization machine learning that
 

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specialization machine learning that
really<00:03:14.670><c> focuses</c><00:03:14.850><c> on</c><00:03:15.480><c> taking</c><00:03:16.250><c> use</c><00:03:17.250><c> cases</c><00:03:17.550><c> and</c>

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really focuses on taking use cases and
 

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really focuses on taking use cases and
opening<00:03:19.050><c> them</c><00:03:19.170><c> up</c><00:03:19.350><c> and</c><00:03:19.620><c> helping</c><00:03:20.519><c> figure</c><00:03:21.360><c> out</c>

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opening them up and helping figure out
 

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opening them up and helping figure out
how<00:03:22.440><c> machine</c><00:03:22.830><c> learning</c><00:03:23.100><c> works</c><00:03:23.370><c> and</c><00:03:23.730><c> why</c>

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you