Carte Ultimate Parallel and Distributed Computing with Julia For Data Science Nabanita Dash

Ultimate Parallel and Distributed Computing with Julia For Data Science

Autor: Nabanita Dash
Limbă: engleză
Legare: Carte broșată
Disponibilitate: În depozitul extern
Expediem în 9-15 zile
209.53 lei
Unleash Julia's power: Code Your Data Stories, Shape Machine Intelligence!Book DescriptionThis book...

Informații despre carte

Limbă
engleză
Legare
Carte - Carte broșată
Publicat
2024
Pagini
486
EAN
9789391246860
ISBN
9391246869
Enbook ID
44681039
Greutate
899
Dimensiuni
191 x 235 x 26

Descriere completă

Unleash Julia's power: Code Your Data Stories, Shape Machine Intelligence!


Book 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.


Table of Contents

1. Julia In Data Science Arena

2. Getting Started with Julia

3. Features Assisting Scaling ML Projects

4. Data Structures in Julia

5. Working With Datasets In Julia

6. Basics of Statistics

7. Probability Data Distributions

8. Framing Data in Julia

9. Working on Data in DataFrames

10. Visualizing Data in Julia

11. Introducing Machine Learning in Julia

12. Data and Models

13. Bayesian Statistics and Modeling

14. Parallel Computation in Julia

15. Distributed Computation in Julia

      Index

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