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1. |
Record Nr. |
UNINA9910983043103321 |
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Autore |
Lin Zhouchen |
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Titolo |
Pattern Recognition and Computer Vision : 7th Chinese Conference, PRCV 2024, Urumqi, China, October 18–20, 2024, Proceedings, Part V / / edited by Zhouchen Lin, Ming-Ming Cheng, Ran He, Kurban Ubul, Wushouer Silamu, Hongbin Zha, Jie Zhou, Cheng-Lin Liu |
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Pubbl/distr/stampa |
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Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2025 |
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ISBN |
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Edizione |
[1st ed. 2025.] |
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Descrizione fisica |
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1 online resource (641 pages) |
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Collana |
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Lecture Notes in Computer Science, , 1611-3349 ; ; 15035 |
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Altri autori (Persone) |
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ChengMing-Ming |
HeRan |
UbulKurban |
SilamuWushouer |
ZhaHongbin |
ZhouJie |
LiuCheng-Lin |
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Disciplina |
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Soggetti |
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Image processing - Digital techniques |
Computer vision |
Artificial intelligence |
Application software |
Computer networks |
Computer systems |
Machine learning |
Computer Imaging, Vision, Pattern Recognition and Graphics |
Artificial Intelligence |
Computer and Information Systems Applications |
Computer Communication Networks |
Computer System Implementation |
Machine Learning |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Sommario/riassunto |
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This 15-volume set LNCS 15031-15045 constitutes the refereed proceedings of the 7th Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2024, held in Urumqi, China, during October 18–20, 2024. The 579 full papers presented were carefully reviewed and selected from 1526 submissions. The papers cover various topics in the broad areas of pattern recognition and computer vision, including machine learning, pattern classification and cluster analysis, neural network and deep learning, low-level vision and image processing, object detection and recognition, 3D vision and reconstruction, action recognition, video analysis and understanding, document analysis and recognition, biometrics, medical image analysis, and various applications. |
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2. |
Record Nr. |
UNINA9910303442303321 |
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Autore |
Chellappan Subhashini |
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Titolo |
Practical Apache Spark : Using the Scala API / / by Subhashini Chellappan, Dharanitharan Ganesan |
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Pubbl/distr/stampa |
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Berkeley, CA : , : Apress : , : Imprint : Apress, , 2018 |
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ISBN |
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Edizione |
[1st ed. 2018.] |
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Descrizione fisica |
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1 online resource (288 pages) |
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Disciplina |
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Soggetti |
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Big data |
Open source software |
Computer programming |
Programming languages (Electronic computers) |
Big Data |
Open Source |
Programming Languages, Compilers, Interpreters |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Sommario/riassunto |
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Work with Apache Spark using Scala to deploy and set up single-node, multi-node, and high-availability clusters. This book discusses various components of Spark such as Spark Core, DataFrames, Datasets and SQL, Spark Streaming, Spark MLib, and R on Spark with the help of practical code snippets for each topic. Practical Apache Spark also covers the integration of Apache Spark with Kafka with examples. You’ll follow a learn-to-do-by-yourself approach to learning – learn the concepts, practice the code snippets in Scala, and complete the assignments given to get an overall exposure. On completion, you’ll have knowledge of the functional programming aspects of Scala, and hands-on expertise in various Spark components. You’ll also become familiar with machine learning algorithms with real-time usage. You will: Discover the functional programming features of Scala Understand the complete architecture of Spark and its components Integrate Apache Spark with Hive and Kafka Use Spark SQL, DataFrames, and Datasets to process data using traditional SQL queries Work with different machine learning concepts and libraries using Spark's MLlib packages. |
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