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00000cgm a2200000Ii 4500 |
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OR_ocn956552237 |
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OCoLC |
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20231017213018.0 |
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m o c |
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cr cna|||||||| |
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vz czazuu |
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160812s2016 xx 142 o vleng d |
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|a UMI
|b eng
|e rda
|e pn
|c UMI
|d OCLCF
|d AU@
|d OCLCO
|d OCLCQ
|d OCLCO
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|a (OCoLC)956552237
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|a CL0500000770
|b Safari Books Online
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|a QA76.9.N38
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049 |
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|a UAMI
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100 |
1 |
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|a Talby, David,
|e on-screen presenter.
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245 |
1 |
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|a Text mining & natural language understanding at Scale :
|b building Scalable NLP pipelines with Scala & Spark /
|c with David Talby & Claudiu Branzan.
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|a Text mining and natural language understanding at Scale
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1 |
|a [Place of publication not identified] :
|b O'Reilly,
|c [2016]
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264 |
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|c ©2016
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300 |
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|a 1 online resource (1 streaming video file (2 hr., 21 min., 33 sec.)) :
|b digital, sound, color
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336 |
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|a two-dimensional moving image
|b tdi
|2 rdacontent
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|a computer
|b c
|2 rdamedia
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|a video
|b v
|2 rdamedia
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|a online resource
|b cr
|2 rdacarrier
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0 |
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|a Presenters, David Talby, Claudiu Branzan.
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|a Title from title screen (viewed August 11, 2016).
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|a "A text mining system must go way beyond indexing and search to appear truly intelligent. First, it should understand language beyond keyword matching. For example, it should be able to distinguish the critical difference between 'Jane has the flu' and 'Jane had the flu when she was 9'. Second, it should be capable of making likely inferences even if they're not explicitly written. For example, inferring that Jan may have the flu if she has had a fever, headache, fatigue, and runny nose for three days. And third, it should do its work as part of a robust, scalable, efficient and easy to extend system. This course teaches software engineers and data scientists how to build intelligent natural language understanding (NLU) based text mining systems at scale using Java, Scala and Spark for distributed processing."--Resource description page.
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590 |
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|a O'Reilly
|b O'Reilly Online Learning: Academic/Public Library Edition
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650 |
|
0 |
|a Natural language processing (Computer science)
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650 |
|
0 |
|a Data mining.
|
650 |
|
0 |
|a Electronic data processing
|x Distributed processing.
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650 |
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2 |
|a Natural Language Processing
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650 |
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2 |
|a Data Mining
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650 |
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6 |
|a Traitement automatique des langues naturelles.
|
650 |
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6 |
|a Exploration de données (Informatique)
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650 |
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6 |
|a Traitement réparti.
|
650 |
|
7 |
|a Data mining
|2 fast
|
650 |
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7 |
|a Electronic data processing
|x Distributed processing
|2 fast
|
650 |
|
7 |
|a Natural language processing (Computer science)
|2 fast
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700 |
1 |
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|a Branzan, Claudiu,
|e on-screen presenter.
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856 |
4 |
0 |
|u https://learning.oreilly.com/videos/~/9781491964309/?ar
|z Texto completo (Requiere registro previo con correo institucional)
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994 |
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|a 92
|b IZTAP
|