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Ultimate Parallel and Distributed Computing with Julia For Data Science

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Title: Ultimate Parallel and Distributed Computing with Julia For Data Science
Description: This book takes you through a step-by-step learning journey, starting with the essentials of Julia's syntax, variables, and functions. You'll unlock the power of efficient data handling by leveraging Julia arrays and DataFrames.jl for insightful analysis. Develop expertise in both basic and advanced statistical models, providing a robust toolkit for deriving meaningful data-driven insights. The journey continues with machine learning proficiency, where you'll implement algorithms confidently using MLJ.jl and MLBase.jl, paving the way for advanced data-driven solutions. Explore the realm of Bayesian inference skills through practical applications using Turing.jl, enhancing your ability to extract valuable insights. The book also introduces crucial Julia packages such as Plots.jl for visualizing data and results. The handbook culminates in optimizing workflows with Julia's parallel and distributed computing capabilities, ensuring efficient and scalable data processing using Distributions.jl, Distributed.jl and SharedArrays.jl. This comprehensive guide equips you with the knowledge and practical insights needed to excel in the dynamic field of data science and machine learning.
Authors: Nabanita Dash
Resource Type: eBook.
Subjects: Computers, Data mining, Business, Business intelligence, Electronic books, Database management, Machine learning
Categories: COMPUTERS / Data Science / Machine Learning
Database: eBook Collection (EBSCOhost)
FullText Links:
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  – Type: ebook-epub
Text:
  Availability: 0
Header DbId: nlebk
DbLabel: eBook Collection (EBSCOhost)
An: 3771462
RelevancyScore: 1123
AccessLevel: 6
PubType: eBook
PubTypeId: ebook
PreciseRelevancyScore: 1122.83581542969
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  Data: This book takes you through a step-by-step learning journey, starting with the essentials of Julia's syntax, variables, and functions. You'll unlock the power of efficient data handling by leveraging Julia arrays and DataFrames.jl for insightful analysis. Develop expertise in both basic and advanced statistical models, providing a robust toolkit for deriving meaningful data-driven insights. The journey continues with machine learning proficiency, where you'll implement algorithms confidently using MLJ.jl and MLBase.jl, paving the way for advanced data-driven solutions. Explore the realm of Bayesian inference skills through practical applications using Turing.jl, enhancing your ability to extract valuable insights. The book also introduces crucial Julia packages such as Plots.jl for visualizing data and results. The handbook culminates in optimizing workflows with Julia's parallel and distributed computing capabilities, ensuring efficient and scalable data processing using Distributions.jl, Distributed.jl and SharedArrays.jl. This comprehensive guide equips you with the knowledge and practical insights needed to excel in the dynamic field of data science and machine learning.
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RecordInfo BibRecord:
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    Classifications:
      – Code: 658.472
        Scheme: ddc
        Type: prePub
    Languages:
      – Code: eng
        Text: English
    Subjects:
      – SubjectFull: Computers
        Type: general
      – SubjectFull: Data mining
        Type: general
      – SubjectFull: Business
        Type: general
      – SubjectFull: Business intelligence
        Type: general
      – SubjectFull: Electronic books
        Type: general
      – SubjectFull: Database management
        Type: general
      – SubjectFull: Machine learning
        Type: general
    Titles:
      – TitleFull: Ultimate Parallel and Distributed Computing with Julia For Data Science
        Type: main
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            NameFull: Nabanita Dash
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          Dates:
            – D: 01
              M: 01
              Type: published
              Y: 2024
            – D: 29
              M: 07
              Type: profile
              Y: 2025
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              Value: 9789391246860
          Titles:
            – TitleFull: Ultimate Parallel and Distributed Computing with Julia For Data Science
              Type: main
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