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191021s2018 cc ab b 001 0 eng d |
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|a 2019301190
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|aGBB7B3690|2bnb
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|a1491974567 (pbk.)
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|a9781491974568 (pbk.) :|cUS64.99
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|a(OCoLC)ocn966394369
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|aYDX|beng|cYDX|dBDX|dBTCTA|dGK8|dSFR|dCLE|dNYP|dSINLB|dOCLCF|dWRF|dEQO|dHCO|dUKMGB|dDLC|dNOU
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|alccopycat
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|aQA76.54|b.L35 2018
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|a004.33|223
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|
|aLB|bLBF|cE020169|dQA76.54|e.L192|y2018|fWJ|n1796|pBook|tLCC
|
100 |
1
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|aLakshmanan, Valliappa.
|
245 |
10
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|aData science on the Google cloud platform :|bimplementing end-to-end real-time data pipelines : from ingest to machine learning /|cValliappa Lakshmanan.
|
250 |
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|a1st ed.
|
260 |
|
|aBeijing ;|aSebastopol, CA :|bO'Reilly Media,|cc2018.
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300 |
|
|axiv, 393 p. :|bill., maps ;|c24 cm.
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504 |
|
|aIncludes bibliographical references and index.
|
505 |
0
|
|aMaking better decisions based on data -- Ingesting data into the cloud -- Creating compelling dashboards -- Streaming data: publication and ingest -- Interactive data exploration -- Bayes classifier on cloud dataproc -- Machine learning: logistic regression on Spark -- Time-windowed aggregate features -- Machine learning classifier using TensorFlow -- Real-time machine learning.
|
520 |
|
|aLearn how easy it is to apply sophisticated statistical and machine learning methods to real-world problems when you build on top of the Google Cloud Platform (GCP). This hands-on guide shows developers entering the data science field how to implement an end-to-end data pipeline, using statistical and machine learning methods and tools on GCP. Over the course of the book, you'll work through a sample business decision by employing a variety of data science approaches. Follow along by implementing these statistical and machine learning solutions in your own project on GCP, and discover how this platform provides a transformative and more collaborative way of doing data science. You'll learn how to: automate and schedule data ingest using an App Engine application, create and populate a dashboard in Google Data Studio, build a real-time analysis pipeline to carry out streaming analytics, conduct interactive data exploration with Google BigQuery, create a Bayesian model on a Cloud Dataproc cluster, build a logistic regression machine learning model with Spark, compute time-aggregate features with a Cloud Dataflow pipeline, create a high-performing prediction model with TensorFlow, use your deployed model as a microservice you can access from both batch and real-time pipelines.--|cSource other than the Library of Congress.
|
610 |
20
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|aGoogle (Firm)
|
630 |
00
|
|aGoogle Apps.
|
650 |
0
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|aReal-time data processing.
|
650 |
0
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|aCloud computing.
|
650 |
0
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|aComputing platforms.
|