Название: Ultimate Parallel and Distributed Computing with Julia For Data Science: Excel in Data Analysis, Statistical Modeling and Machine Learning by Leveraging MLBase.jl and MLJ.jl to Optimize WorkflowsАвтор: Nabanita Dash
Издательство: Orange Education Pvt Ltd, AVA
Год: 2024
Страниц: 484
Язык: английский
Формат: epub (true)
Размер: 10.1 MB
Unleash Julia’s power: Code Your Data Stories, Shape Machine Intelligence!
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.
In this book, we embark on a journey that explores the powerful intersection of Julia, a high-performance programming language, and the transformative fields of machine learning and data analysis. As technology continues to evolve, the ability to harness and interpret vast amounts of data becomes increasingly crucial. Julia, with its speed, simplicity, and expressive syntax, emerges as an ideal language for tackling the challenges posed by the ever-growing datasets and complex algorithms in these domains.
This book is designed to be a comprehensive guide for both beginners and experienced practitioners seeking to master Machine Learning and data analysis using Julia. Whether you are a seasoned data scientist or a curious enthusiast, the content here will equip you with the tools and knowledge to navigate the dynamic landscape of modern Data Science.
Chapter 1: Julia in ML Fields
In our opening chapter, we lay the foundation by introducing readers to Julia as the optimal language for machine learning. We delve into the essence of data science, elucidate the necessity of data analysis, and showcase Julia's prowess in the realm of machine learning. While extolling the virtues of Julia, we candidly explore its strengths and acknowledge its limitations, culminating in a thoughtful conclusion.
Chapter 2: Get Started with Julia
This chapter serves as your gateway into the Julia ecosystem. We guide you through the installation process, introduce various Julia Integrated Development Environments (IDEs), and delve into the essentials of Julia's package management system. Building a solid foundation, we cover topics ranging from basic variable declarations to defining functions and navigating the intricate rules of variable scope.
Chapter 3: Features Assisting Scaling ML Projects
Discover how Julia's unique features enhance the scalability of machine learning projects. We explore the type system, multiple dispatch, and the intricacies of working with packages, modules, and macros. This chapter equips you with the tools needed to harness Julia's full potential for robust and scalable machine learning endeavors.
Chapter 4: Working with Julia Arrays
Delve into the world of Julia data structures. From arrays and tuple structures to advanced concepts like broadcasting and integration with Python/R, this chapter ensures a comprehensive understanding of Julia's array capabilities.
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Chapter 14: Parallel Computation in Julia
Master the art of parallel computation in Julia, leveraging multi-threading capabilities to speed up data science workflows. Discover the potential of JuliaHub and conclude with insights into optimizing code execution.
Chapter 15: Distributed Computing within Julia
Explore distributed computing in Julia, focusing on scaling data science and machine learning problems on CPUs. Dive into Dagger.jl, drawing parallels with Dask, and gain an introduction to CUDA.jl and CuArrays for GPU programming.
Contents:
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