Next -generation machine learning with spark: Covers XGBoost, lightGBM, spark NLP, distributed deep learning with Keras and more (Record no. 297448)

MARC details
000 -LEADER
fixed length control field 02045nam a22001697a 4500
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9781484267684
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 006.31
Item number QUI-N
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Quinto, Butch
245 ## - TITLE STATEMENT
Title Next -generation machine learning with spark: Covers XGBoost, lightGBM, spark NLP, distributed deep learning with Keras and more
Statement of responsibility, etc. By Butch Quinto
250 ## - EDITION STATEMENT
Edition statement 1
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Name of publisher, distributor, etc. Apress,
Date of publication, distribution, etc. c2020.
300 ## - PHYSICAL DESCRIPTION
Extent i-xix+355P.
505 ## - FORMATTED CONTENTS NOTE
Formatted contents note 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 ## - SUMMARY, ETC.
Summary, etc. 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 .
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Big data- machine learning
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type Book
Holdings
Withdrawn status Lost status Damaged status Not for loan Home library Current library Shelving location Date acquired Total Checkouts Full call number Barcode Date last seen Price effective from Koha item type
        Dept. of Computational Biology and Bioinformatics Dept. of Computational Biology and Bioinformatics Processing Center 05/10/2021   006.31 QUI-N DCB3965 05/10/2021 05/10/2021 Book