Language:
English
繁體中文
Help
Login
Back
Switch To:
Labeled
|
MARC Mode
|
ISBD
Learn PySpark = Build Python-based M...
~
Singh, Pramod.
Learn PySpark = Build Python-based Machine Learning and Deep Learning Models /
Record Type:
Language materials, printed : Monograph/item
Title/Author:
Learn PySpark/ by Pramod Singh.
Reminder of title:
Build Python-based Machine Learning and Deep Learning Models /
Author:
Singh, Pramod.
Description:
XVIII, 210 p. 187 illus., 32 illus. in color.online resource. :
Contained By:
Springer Nature eBook
Subject:
Python (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 PySpark
Build Python-based Machine Learning and Deep Learning Models /[electronic resource] :by Pramod Singh. - 1st ed. 2019. - XVIII, 210 p. 187 illus., 32 illus. in color.online resource.
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:
1127623
Python (Computer program language).
LC Class. No.: QA76.73.P98
Dewey Class. No.: 005.133
Learn PySpark = Build Python-based Machine Learning and Deep Learning Models /
LDR
:02583nam a22003855i 4500
001
1011051
003
DE-He213
005
20200703081822.0
007
cr nn 008mamaa
008
210106s2019 xxu| s |||| 0|eng d
020
$a
9781484249611
$9
978-1-4842-4961-1
024
7
$a
10.1007/978-1-4842-4961-1
$2
doi
035
$a
978-1-4842-4961-1
050
4
$a
QA76.73.P98
072
7
$a
UMX
$2
bicssc
072
7
$a
COM051360
$2
bisacsh
072
7
$a
UMX
$2
thema
082
0 4
$a
005.133
$2
23
100
1
$a
Singh, Pramod.
$e
author.
$4
aut
$4
http://id.loc.gov/vocabulary/relators/aut
$3
1305189
245
1 0
$a
Learn PySpark
$h
[electronic resource] :
$b
Build Python-based Machine Learning and Deep Learning Models /
$c
by Pramod Singh.
250
$a
1st ed. 2019.
264
1
$a
Berkeley, CA :
$b
Apress :
$b
Imprint: Apress,
$c
2019.
300
$a
XVIII, 210 p. 187 illus., 32 illus. in color.
$b
online resource.
336
$a
text
$b
txt
$2
rdacontent
337
$a
computer
$b
c
$2
rdamedia
338
$a
online resource
$b
cr
$2
rdacarrier
347
$a
text file
$b
PDF
$2
rda
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
Python (Computer program language).
$3
1127623
650
0
$a
Big data.
$3
981821
650
0
$a
Machine learning.
$3
561253
650
0
$a
Open source software.
$3
561177
650
0
$a
Computer programming.
$3
527822
650
1 4
$a
Python.
$3
1115944
650
2 4
$a
Big Data.
$3
1017136
650
2 4
$a
Machine Learning.
$3
1137723
650
2 4
$a
Open Source.
$3
1113081
710
2
$a
SpringerLink (Online service)
$3
593884
773
0
$t
Springer Nature eBook
776
0 8
$i
Printed edition:
$z
9781484249604
776
0 8
$i
Printed edition:
$z
9781484249628
856
4 0
$u
https://doi.org/10.1007/978-1-4842-4961-1
912
$a
ZDB-2-CWD
912
$a
ZDB-2-SXPC
950
$a
Professional and Applied Computing (SpringerNature-12059)
950
$a
Professional and Applied Computing (R0) (SpringerNature-43716)
based on 0 review(s)
Multimedia
Reviews
Add a review
and share your thoughts with other readers
Export
pickup library
Processing
...
Change password
Login