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so<00:00:14.219><c> yeah</c><00:00:14.639><c> my</c><00:00:14.879><c> name</c><00:00:15.000><c> is</c><00:00:15.090><c> Joo</c><00:00:15.299><c> ho-young</c><00:00:15.480><c> and</c><00:00:15.869><c> I'm</c>

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so yeah my name is Joo ho-young and I'm
 

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so yeah my name is Joo ho-young and I'm
from<00:00:16.529><c> the</c><00:00:16.710><c> bigoted</c><00:00:17.039><c> happen</c><00:00:17.460><c> ology</c><00:00:17.880><c> Department</c>

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from the bigoted happen ology Department
 

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from the bigoted happen ology Department
of<00:00:18.630><c> Intel</c><00:00:19.290><c> and</c><00:00:19.880><c> currently</c><00:00:20.880><c> I'm</c><00:00:21.060><c> focusing</c><00:00:21.600><c> on</c>

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of Intel and currently I'm focusing on
 

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of Intel and currently I'm focusing on
developing<00:00:22.350><c> large-scale</c><00:00:22.950><c> deep</c><00:00:23.160><c> learning</c>

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developing large-scale deep learning
 

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developing large-scale deep learning
applications<00:00:24.090><c> on</c><00:00:24.480><c> spark</c><00:00:24.930><c> cluster</c><00:00:31.699><c> ok</c>

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applications on spark cluster ok
 

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applications on spark cluster ok
currently<00:00:33.199><c> building</c><00:00:34.199><c> a</c><00:00:34.530><c> library</c><00:00:34.770><c> called</c><00:00:35.250><c> big</c>

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currently building a library called big
 

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currently building a library called big
DL<00:00:35.940><c> and</c><00:00:36.239><c> it's</c><00:00:36.960><c> for</c><00:00:38.030><c> helping</c><00:00:39.030><c> users</c><00:00:39.600><c> to</c>

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DL and it's for helping users to
 

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DL and it's for helping users to
developing<00:00:40.590><c> deeper</c><00:00:40.920><c> deep</c><00:00:41.700><c> learning</c>

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developing deeper deep learning
 

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developing deeper deep learning
applications<00:00:42.690><c> on</c><00:00:42.930><c> spark</c><00:00:43.470><c> lancers</c><00:00:43.980><c> so</c><00:00:44.790><c> we</c><00:00:45.060><c> want</c>

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applications on spark lancers so we want
 

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applications on spark lancers so we want
to<00:00:45.600><c> approve</c><00:00:45.989><c> wipped</c><00:00:46.800><c> users</c><00:00:47.220><c> advise</c><00:00:47.730><c> the</c>

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to approve wipped users advise the
 

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to approve wipped users advise the
capacitance<00:00:49.070><c> inhale</c><00:00:50.070><c> based</c><00:00:52.010><c> architecture</c><00:00:53.010><c> so</c>

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capacitance inhale based architecture so
 

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capacitance inhale based architecture so
we<00:00:55.500><c> are</c><00:00:55.739><c> trying</c><00:00:56.280><c> to</c><00:00:56.550><c> resolve</c><00:00:57.090><c> the</c><00:00:57.329><c> issue</c>

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we are trying to resolve the issue
 

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we are trying to resolve the issue
including<00:00:57.899><c> like</c><00:00:58.559><c> large</c><00:00:59.040><c> security</c><00:00:59.340><c> planning</c>

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including like large security planning
 

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including like large security planning
implications<00:01:00.570><c> and</c><00:01:01.010><c> we</c><00:01:02.010><c> want</c><00:01:02.250><c> to</c><00:01:02.660><c> achieve</c><00:01:03.660><c> with</c>

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implications and we want to achieve with
 

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implications and we want to achieve with
a<00:01:04.110><c> function</c><00:01:04.619><c> parity</c><00:01:05.040><c> with</c><00:01:05.280><c> other</c><00:01:05.600><c> like</c><00:01:06.600><c> a</c>

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a function parity with other like a
 

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a function parity with other like a
popular<00:01:07.229><c> deep</c><00:01:07.469><c> learning</c><00:01:07.619><c> frameworks</c>

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popular deep learning frameworks
 

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popular deep learning frameworks
tensorflow<00:01:09.179><c> cafe</c><00:01:09.810><c> and</c><00:01:10.200><c> others</c><00:01:10.889><c> so</c><00:01:11.759><c> the</c><00:01:12.329><c> goal</c>

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tensorflow cafe and others so the goal
 

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tensorflow cafe and others so the goal
here<00:01:12.630><c> is</c><00:01:13.079><c> really</c><00:01:13.109><c> to</c><00:01:13.499><c> achieve</c><00:01:14.509><c> well</c><00:01:15.509><c> to</c><00:01:16.439><c> help</c>

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here is really to achieve well to help
 

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here is really to achieve well to help
user<00:01:16.859><c> to</c><00:01:17.280><c> achieve</c><00:01:17.579><c> the</c><00:01:17.789><c> most</c><00:01:17.939><c> of</c><00:01:18.240><c> which</c><00:01:18.719><c> big</c>

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user to achieve the most of which big
 

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user to achieve the most of which big
deal<00:01:19.200><c> for</c><00:01:19.679><c> their</c><00:01:20.069><c> deep</c><00:01:20.310><c> learning</c>

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deal for their deep learning
 

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deal for their deep learning
applications<00:01:28.009><c> actually</c><00:01:29.009><c> scale</c><00:01:29.399><c> circle</c>

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applications actually scale circle
 

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applications actually scale circle
ability<00:01:30.299><c> is</c><00:01:30.359><c> our</c><00:01:31.249><c> stress</c><00:01:32.249><c> actually</c><00:01:33.240><c> for</c><00:01:33.689><c> video</c>

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ability is our stress actually for video
 

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ability is our stress actually for video
and<00:01:34.439><c> since</c><00:01:35.099><c> we</c><00:01:35.219><c> built</c><00:01:35.459><c> a</c><00:01:35.670><c> deep</c><00:01:35.819><c> learning</c>

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and since we built a deep learning
 

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and since we built a deep learning
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applications on spark so it provides
 

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applications on spark so it provides
some<00:01:40.259><c> nature</c><00:01:40.939><c> scale-out</c><00:01:42.109><c> capacity</c><00:01:43.109><c> so</c>

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some nature scale-out capacity so
 

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some nature scale-out capacity so
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okay<00:01:53.980><c> it's</c><00:01:54.400><c> a</c><00:01:54.460><c> network</c><00:01:54.880><c> communication</c>

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okay it's a network communication
 

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okay it's a network communication
actually<00:01:56.490><c> we</c><00:01:57.490><c> need</c><00:01:58.240><c> to</c><00:01:58.480><c> transfer</c><00:01:59.170><c> some</c>

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actually we need to transfer some
 

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actually we need to transfer some
greetings<00:02:01.360><c> and</c><00:02:01.780><c> larger</c><00:02:02.650><c> models</c><00:02:03.070><c> through</c><00:02:04.000><c> the</c>

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greetings and larger models through the
 

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greetings and larger models through the
networks<00:02:04.660><c> and</c><00:02:05.230><c> your</c><00:02:05.620><c> rage</c><00:02:05.950><c> can</c><00:02:06.760><c> cost</c><00:02:07.180><c> us</c><00:02:07.420><c> some</c>

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networks and your rage can cost us some
 

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networks and your rage can cost us some
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issues for well sometimes the network is
 

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issues for well sometimes the network is
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not stable and sometimes a space is just
 

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not stable and sometimes a space is just
too<00:02:14.500><c> slow</c>

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too slow
 

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too slow
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yes primary issue for us
 

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yes primary issue for us
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well<00:02:24.890><c> it</c><00:02:25.280><c> has</c><00:02:25.580><c> a</c><00:02:26.180><c> lot</c><00:02:26.360><c> you</c><00:02:26.780><c> know</c><00:02:26.810><c> we're</c>

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well it has a lot you know we're
 

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well it has a lot you know we're
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primarily using Scala and we found it's
 

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primarily using Scala and we found it's
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quite productive for allocation and for
 

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quite productive for allocation and for
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our customers actually a lot of
 

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our customers actually a lot of
customers<00:02:36.349><c> loves</c><00:02:37.099><c> to</c><00:02:37.819><c> use</c><00:02:38.000><c> spark</c><00:02:38.390><c> because</c><00:02:38.870><c> of</c>

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customers loves to use spark because of
 

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customers loves to use spark because of
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well it's type safe and this efficient
 

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well it's type safe and this efficient
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is possible on Tyrion and the
 

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is possible on Tyrion and the
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programming interface you are they easy
 

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programming interface you are they easy
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well it's a quite efficient I can meet a
 

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well it's a quite efficient I can meet a
lot<00:03:03.130><c> of</c><00:03:03.210><c> fellows</c><00:03:04.210><c> in</c><00:03:04.480><c> the</c><00:03:04.660><c> same</c><00:03:04.840><c> industry</c><00:03:05.170><c> and</c>

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lot of fellows in the same industry and
 

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lot of fellows in the same industry and
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learn a lot from other companies and
 

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learn a lot from other companies and
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experice and I can also you know catch
 

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experice and I can also you know catch
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some opportunity for promoting big deal
 

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some opportunity for promoting big deal
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