Next-Generation Machine Learning with Spark : Covers XGBoost, LightGBM, Spark NLP, Distributed Deep Learning with Keras, and More / by Butch Quinto.

Format:

Book

Author/Creator:

Quinto, Butch., Author.

Language:

English

Subjects (All):

Spark (Electronic resource : Apache Software Foundation).

Big data.

Big Data.

Local Subjects:

Big Data.

Physical Description:

1 online resource (XIX, 355 p. 67 illus.)

Edition:

1st ed. 2020.

Place of Publication:

Berkeley, CA : Apress : Imprint: Apress, 2020.

System Details:

Mode of access: World Wide Web.

text file

Summary:

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 .

Contents:

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.-.

Notes:

Includes index.

Includes bibliographical references.

ISBN:

9781484256695

1484256697

OCLC:

1179144121