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Learn PySpark : build Python-based machine learning and deep learning models /
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,...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
New York :
Apress,
[2019]
|
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 100 | 1 | |a Singh, Pramod, |e author. | |
| 245 | 1 | 0 | |a Learn PySpark : |b build Python-based machine learning and deep learning models / |c Pramod Singh. |
| 264 | 1 | |a New York : |b Apress, |c [2019] | |
| 264 | 4 | |c ©2019 | |
| 300 | |a 1 online resource : |b illustrations (some color) | ||
| 336 | |a text |b txt |2 rdacontent | ||
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| 347 | |a text file | ||
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| 588 | 0 | |a Online resource; title from PDF title page (SpringerLink, viewed September 23, 2019). | |
| 500 | |a Includes index. | ||
| 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 Sparks 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. | ||
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a SPARK (Computer program language) | |
| 650 | 0 | |a Python (Computer program language) | |
| 650 | 0 | |a Machine learning. | |
| 650 | 6 | |a Python (Langage de programmation) | |
| 650 | 6 | |a Apprentissage automatique. | |
| 650 | 7 | |a Machine learning. |2 fast |0 (OCoLC)fst01004795 | |
| 650 | 7 | |a Python (Computer program language) |2 fast |0 (OCoLC)fst01084736 | |
| 650 | 7 | |a SPARK (Computer program language) |2 fast |0 (OCoLC)fst01922197 | |
| 776 | 0 | 8 | |i Print version: |a Singh, Pramod. |t Learn Pyspark : Build Python-Based Machine Learning and Deep Learning Models. |d Berkeley, CA : Apress L.P., ©2019 |z 9781484249604 |
| 856 | 4 | 0 | |u https://learning.oreilly.com/library/view/~/9781484249611/?ar |z Texto completo (Requiere registro previo con correo institucional) |
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