東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Learn PySpark = build Python-based m...

Singh, Pramod.

Linked to FindBook

Google Book

Amazon

博客來

Learn PySpark = build Python-based machine learning and deep learning models /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Learn PySpark/ by Pramod Singh.
Reminder of title:	build Python-based machine learning and deep learning models /
Author:	Singh, Pramod.
Published:	Berkeley, CA :Apress : : 2019.,
Description:	xviii, 210 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	Chapter 1: Introduction to PySpark -- Chapter 2: Data Processing -- Chapter 3: Spark Structured Streaming -- Chapter 4: Airflow -- Chapter 5: Machine Learning Library (MLlib) -- Chapter 6: Supervised Machine Learning -- Chapter 7: Unsupervised Machine Learning -- Chapter 8: Deep Learning Using PySpark.
Contained By:	Springer Nature eBook
Subject:	SPARK (Computer program language) -
Online resource:	https://doi.org/10.1007/978-1-4842-4961-1
ISBN:	9781484249611

Learn PySpark = build Python-based machine learning and deep learning models /
Singh, Pramod.

Learn PySparkbuild Python-based machine learning and deep learning models /[electronic resource] :by Pramod Singh. - Berkeley, CA :Apress :2019. - xviii, 210 p. :ill. (some col.), digital ;24 cm.

Chapter 1: Introduction to PySpark -- Chapter 2: Data Processing -- Chapter 3: Spark Structured Streaming -- Chapter 4: Airflow -- Chapter 5: Machine Learning Library (MLlib) -- Chapter 6: Supervised Machine Learning -- Chapter 7: Unsupervised Machine Learning -- Chapter 8: Deep Learning Using PySpark.

Leverage machine and deep learning models to build applications on real-time data using PySpark. This book is perfect for those who want to learn to use this language to perform exploratory data analysis and solve an array of business challenges. You'll start by reviewing PySpark fundamentals, such as Spark's core architecture, and see how to use PySpark for big data processing like data ingestion, cleaning, and transformations techniques. This is followed by building workflows for analyzing streaming data using PySpark and a comparison of various streaming platforms. You'll then see how to schedule different spark jobs using Airflow with PySpark and book examine tuning machine and deep learning models for real-time predictions. This book concludes with a discussion on graph frames and performing network analysis using graph algorithms in PySpark. All the code presented in the book will be available in Python scripts on Github.

ISBN: 9781484249611

Standard No.: 10.1007/978-1-4842-4961-1doiSubjects--Topical Terms:

3300294
SPARK (Computer program language)

LC Class. No.: QA76.73.S59 / S56 2019

Dewey Class. No.: 006.31

Learn PySpark = build Python-based machine learning and deep learning models /
LDR:02284nmm a2200325 a 4500 001 2243307
003 DE-He213
005 20200703081822.0
006 m d
007 cr nn 008maaau
008 211207s2019 cau s 0 eng d
020 $a 9781484249611 $q (electronic bk.)
020 $a 9781484249604 $q (paper)
024 7 $a 10.1007/978-1-4842-4961-1 $2 doi
035 $a 978-1-4842-4961-1
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.73.S59 $b S56 2019
072 7 $a UMX $2 bicssc
072 7 $a COM051360 $2 bisacsh
072 7 $a UMX $2 thema
082 0 4 $a 006.31 $2 23
090 $a QA76.73.S59 $b S617 2019
100 1 $a Singh, Pramod. $3 3384003
245 1 0 $a Learn PySpark $h [electronic resource] : $b build Python-based machine learning and deep learning models / $c by Pramod Singh.
260 $a Berkeley, CA : $b Apress : $b Imprint: Apress, $c 2019.
300 $a xviii, 210 p. : $b ill. (some col.), digital ; $c 24 cm.
505 0 $a Chapter 1: Introduction to PySpark -- Chapter 2: Data Processing -- Chapter 3: Spark Structured Streaming -- Chapter 4: Airflow -- Chapter 5: Machine Learning Library (MLlib) -- Chapter 6: Supervised Machine Learning -- Chapter 7: Unsupervised Machine Learning -- Chapter 8: Deep Learning Using PySpark.
520 $a Leverage machine and deep learning models to build applications on real-time data using PySpark. This book is perfect for those who want to learn to use this language to perform exploratory data analysis and solve an array of business challenges. You'll start by reviewing PySpark fundamentals, such as Spark's core architecture, and see how to use PySpark for big data processing like data ingestion, cleaning, and transformations techniques. This is followed by building workflows for analyzing streaming data using PySpark and a comparison of various streaming platforms. You'll then see how to schedule different spark jobs using Airflow with PySpark and book examine tuning machine and deep learning models for real-time predictions. This book concludes with a discussion on graph frames and performing network analysis using graph algorithms in PySpark. All the code presented in the book will be available in Python scripts on Github.
650 0 $a SPARK (Computer program language) $3 3300294
650 0 $a Python (Computer program language) $3 729789
650 0 $a Machine learning. $3 533906
650 1 4 $a Python. $3 3201289
650 2 4 $a Big Data. $3 3134868
650 2 4 $a Machine Learning. $3 3382522
650 2 4 $a Open Source. $3 2210577
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-1-4842-4961-1
950 $a Professional and Applied Computing (SpringerNature-12059)