Ultimate Parallel and Distributed Computing with Julia for Data Science

• Record Nr.: UNINA9911008938103321
• Autore: Dash Nabanita
• Pubbl/distr/stampa: Delhi : , : Orange Education PVT Ltd, , 2024; ©2023
• ISBN: 9789391246860; 9391246869
• Edizione: [1st ed.]
• Descrizione fisica: 1 online resource (272 pages)
• Soggetti: Julia (Computer program language); Machine learning
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di contenuto: Cover Page -- Title Page -- Copyright Page -- Dedication Page -- About the Author -- About the Technical Reviewers -- Acknowledgements -- Preface -- Errata -- Table of Contents -- 1. Julia In Data Science Arena -- Introduction -- Structure -- Introducing Data Science -- Defining Data Science -- The need for Task Automation -- Introducing Statistics -- Introducing machine learning -- Drawing Correlations from Raw Data -- Explaining the Need for Data Analysis -- Introducing Julia -- Astounding Julia language!! -- Julia: Ideal for Data Analysis -- Limitations of Julia -- Conclusion -- Points to Remember -- References -- 2. Getting Started with Julia -- Introduction -- Structure
• Sommario/riassunto: 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.