| 書目名稱 | Learn PySpark | | 副標(biāo)題 | Build Python-based M | | 編輯 | Pramod Singh | | 視頻video | http://file.papertrans.cn/583/582623/582623.mp4 | | 概述 | Covers entire range of PySpark’s offerings from streaming to graph analytics.Build standardized work flows for pre-processing and builds machine learning and deep learning models on big data sets.Disc | | 圖書封面 |  | | 描述 | 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..What You‘ll Learn.Develop pipelines for streaming data processing using PySpark?.Build Machine Learning & Deep Learning models using PySpark latest offerings.Use graph analytics using PySpark?.Create Sequence Embeddings from Text data?.Who This Book is For? | | 出版日期 | Book 2019 | | 關(guān)鍵詞 | PySpark; Python; Machine Learning; Deep Learning; Big Data; Spark; Data Processing; AirFlow; Supervised Mach | | 版次 | 1 | | doi | https://doi.org/10.1007/978-1-4842-4961-1 | | isbn_softcover | 978-1-4842-4960-4 | | isbn_ebook | 978-1-4842-4961-1 | | copyright | Pramod Singh 2019 |
The information of publication is updating
|
|
|Archiver|手機(jī)版|小黑屋|
派博傳思國際
( 京公網(wǎng)安備110108008328)
GMT+8, 2025-10-9 19:52
發(fā)表于 2025-3-21 18:45:55
