Masood Learning F# Functional Data Structures and Algorithms

Get started with F# and explore functional programming paradigm with data structures and algorithms
1. Auflage 2025
ISBN: 978-1-78355-385-3
Verlag: De Gruyter
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 206 Seiten

Key FeaturesBook DescriptionIf you have just started your adventure with F#, then this book will help you take the right steps to become a successful F# coder. An intermediate knowledge of imperative programming concepts, and a basic understanding of the algorithms and data structures in.NET environments using the C# language and BCL (Base Class Library), would be helpful.What you will learn - Familiarize yourself with the functional programming nature of F# and explore its fundamentals
- Utilize data structures available in F# and apply recursion and lazy evaluation
- Gain insights into functional programming paradigms; dissect F# code and analyze code available in community projects
- Build abstract data structures and utilize powerful optimization techniques such as memoization
- Explore and test builtin F# bespoke data structures and algorithms
- Become resourceful and learn how to easily reuse libraries contributed by the C# and F# community
- Understand the tradeoffs in selecting purely functional (persistent) over mutable data structures
- Implement custom ADT (Abstract Data Type), and discover parallel programming and asynchrony within F#
Who this book is forIf you have just started your adventure with F#, then this book will help you take the right steps to become a successful F# coder. An intermediate knowledge of imperative programming concepts, and a basic understanding of the algorithms and data structures in.NET environments using the C# language and BCL (Base Class Library), would be helpful.

Autoren/Hrsg.

Masood, Adnan

Fachgebiete

• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Algorithmen & Datenstrukturen

Über Autor(innen)

Masood Adnan :

Adnan Masood, PhD is an artificial intelligence and machine learning researcher, visiting scholar at Stanford AI Lab, software engineer, Microsoft MVP (Most Valuable Professional), and Microsoft's regional director for artificial intelligence. As chief architect of AI and machine learning at UST Global, he collaborates with Stanford AI Lab and MIT CSAIL, and leads a team of data scientists and engineers building artificial intelligence solutions to produce business value and insights that affect a range of businesses, products, and initiatives.