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Next-generation machine learning wit...

Quinto, Butch.

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Next-generation machine learning with Spark = Covers XGBoost, LightGBM, Spark NLP, Distributed deep learning with Keras, and more /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Next-generation machine learning with Spark/ by Butch Quinto.
Reminder of title:	Covers XGBoost, LightGBM, Spark NLP, Distributed deep learning with Keras, and more /
Author:	Quinto, Butch.
Published:	Berkeley, CA :Apress : : 2020.,
Description:	xix, 355 p. :ill., digital ;24 cm.
[NT 15003449]:	Chapter 1: Introduction to Machine Learning -- Chapter 2: Introduction to Spark and Spark Mllib -- Chapter 3: Supervised Learning -- Chapter 4: Unsupervised Learning -- Chapter 5: Recommendations -- Chapter 6: Graph Analysis -- Chapter 7: Deep Learning.
Contained By:	Springer eBooks
Subject:	Machine learning. -
Online resource:	https://doi.org/10.1007/978-1-4842-5669-5
ISBN:	9781484256695

Next-generation machine learning with Spark = Covers XGBoost, LightGBM, Spark NLP, Distributed deep learning with Keras, and more /
Quinto, Butch.

Next-generation machine learning with SparkCovers XGBoost, LightGBM, Spark NLP, Distributed deep learning with Keras, and more /[electronic resource] :by Butch Quinto. - Berkeley, CA :Apress :2020. - xix, 355 p. :ill., digital ;24 cm.

Chapter 1: Introduction to Machine Learning -- Chapter 2: Introduction to Spark and Spark Mllib -- Chapter 3: Supervised Learning -- Chapter 4: Unsupervised Learning -- Chapter 5: Recommendations -- Chapter 6: Graph Analysis -- Chapter 7: Deep Learning.

Access real-world documentation and examples for the Spark platform for building large-scale, enterprise-grade machine learning applications. The past decade has seen an astonishing series of advances in machine learning. These breakthroughs are disrupting our everyday life and making an impact across every industry. Next-Generation Machine Learning with Spark provides a gentle introduction to Spark and Spark MLlib and advances to more powerful, third-party machine learning algorithms and libraries beyond what is available in the standard Spark MLlib library. By the end of this book, you will be able to apply your knowledge to real-world use cases through dozens of practical examples and insightful explanations. You will: Be introduced to machine learning, Spark, and Spark MLlib 2.4.x Achieve lightning-fast gradient boosting on Spark with the XGBoost4J-Spark and LightGBM libraries Detect anomalies with the Isolation Forest algorithm for Spark Use the Spark NLP and Stanford CoreNLP libraries that support multiple languages Optimize your ML workload with the Alluxio in-memory data accelerator for Spark Use GraphX and GraphFrames for Graph Analysis Perform image recognition using convolutional neural networks Utilize the Keras framework and distributed deep learning libraries with Spark.

ISBN: 9781484256695

Standard No.: 10.1007/978-1-4842-5669-5doiSubjects--Uniform Titles:

SPARK (Electronic resource)
Subjects--Topical Terms:

533906
Machine learning.

LC Class. No.: Q325.5 / .Q85 2020

Dewey Class. No.: 006.31

Next-generation machine learning with Spark = Covers XGBoost, LightGBM, Spark NLP, Distributed deep learning with Keras, and more /
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505 0 $a Chapter 1: Introduction to Machine Learning -- Chapter 2: Introduction to Spark and Spark Mllib -- Chapter 3: Supervised Learning -- Chapter 4: Unsupervised Learning -- Chapter 5: Recommendations -- Chapter 6: Graph Analysis -- Chapter 7: Deep Learning.
520 $a Access real-world documentation and examples for the Spark platform for building large-scale, enterprise-grade machine learning applications. The past decade has seen an astonishing series of advances in machine learning. These breakthroughs are disrupting our everyday life and making an impact across every industry. Next-Generation Machine Learning with Spark provides a gentle introduction to Spark and Spark MLlib and advances to more powerful, third-party machine learning algorithms and libraries beyond what is available in the standard Spark MLlib library. By the end of this book, you will be able to apply your knowledge to real-world use cases through dozens of practical examples and insightful explanations. You will: Be introduced to machine learning, Spark, and Spark MLlib 2.4.x Achieve lightning-fast gradient boosting on Spark with the XGBoost4J-Spark and LightGBM libraries Detect anomalies with the Isolation Forest algorithm for Spark Use the Spark NLP and Stanford CoreNLP libraries that support multiple languages Optimize your ML workload with the Alluxio in-memory data accelerator for Spark Use GraphX and GraphFrames for Graph Analysis Perform image recognition using convolutional neural networks Utilize the Keras framework and distributed deep learning libraries with Spark.
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