Mastering Julia. Enhance your analytical and programming skills for data modeling and processing with Julia
2nd edition

Par : Malcolm Sherrington
  • Nombre de pages487
  • FormatGrand Format
  • PrésentationBroché
  • Poids0.93 kg
  • Dimensions19,0 cm × 23,5 cm × 2,5 cm
  • ISBN978-1-80512-979-0
  • EAN9781805129790
  • Date de parution19/01/2024
  • ÉditeurPackt Publishing

Résumé

Julia is a well-constructed programming language that was designed for fast execution speeds by using just-in-time LLVM compilation techniques, thus eliminating the classic problem of performing analysis in one language and translating it for performance in a second language. This book is a primer on Julia's approach to a wide variety of topics such as scientific computing, statistics, machine learning, simulation, graphics, and distributed computing.
Starting off with a refresher on installing and running Julia on different platforms, you'll quickly get to grips with the core concepts and delve into a discussion on how to use Julia with various code editors and interactive development environments (IDEs). As you progress, you'll see how data works through simple statistics and analytics and discover Julia's speed, its real strength, which makes it particularly useful in highly intensive computing tasks.
You'll also observe how Julia can cooperate with external processes to enhance graphics and data visualizations. Finally, you will explore metaprogramming and learn how it adds great power to the language and establishes networking and distributed computing with Julia. By the end of this book, you'll be confident in using Julia as part of your existing skill set.

L'éditeur en parle

What you will learn : Get to grips with Julia's type system, multiple dispatch, metaprogramming, and macro development. Interact with data files, tables, data frames, SQL, and NoSQL databases. Delve into statistical analytics, linear programming, and optimization problems. Create graphics and visualizations to enhance modeling and simulation in Julia. Understand Julia's main approaches to machine learning, Bayesian analysis, and Al.
About the author : Malcolm Sherrington has been working in computing for over 35 years. He holds degrees in mathematics, chemistry, and engineering and has given lectures at two different universities in the UK as well as worked in the aerospace and healthcare industries. Currently, he is running his own company in the finance sector, with specific interests in High Performance Computing and applications of GPUs and parallelism.
Always hands-on, Malcolm started programming scientific problems in Fortran and C, progressing through Ada and Common Lisp, and recently became involved with data processing and analytics in Perl, Python, and R. Malcolm is the organizer of the London Julia User Group. In addition, he is a co-organizer of the UK High Performance Computing and the financial engineers and Quant London meetup groups.