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Hands-On Data Structures and Algorithms with Rust

Hands-On Data Structures and Algorithms with Rust

By : Claus Matzinger
2.7 (3)
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Hands-On Data Structures and Algorithms with Rust

Hands-On Data Structures and Algorithms with Rust

2.7 (3)
By: Claus Matzinger

Overview of this book

Rust has come a long way and is now utilized in several contexts. Its key strengths are its software infrastructure and resource-constrained applications, including desktop applications, servers, and performance-critical applications, not forgetting its importance in systems' programming. This book will be your guide as it takes you through implementing classic data structures and algorithms in Rust, helping you to get up and running as a confident Rust programmer. The book begins with an introduction to Rust data structures and algorithms, while also covering essential language constructs. You will learn how to store data using linked lists, arrays, stacks, and queues. You will also learn how to implement sorting and searching algorithms. You will learn how to attain high performance by implementing algorithms to string data types and implement hash structures in algorithm design. The book will examine algorithm analysis, including Brute Force algorithms, Greedy algorithms, Divide and Conquer algorithms, Dynamic Programming, and Backtracking. By the end of the book, you will have learned how to build components that are easy to understand, debug, and use in different applications.
Table of Contents (15 chapters)
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Finding the best

The search domain is present on various levels of abstraction: finding a word in a body of text is typically more complex than simply calling the contains() function, and if there are several results, which is the one that was searched for? This entire class of problem is summed up under the umbrella of information retrieval, where problems of ranking, indexing, understanding, storing, and searching are solved in order to retrieve the optimum result (for all definitions). This chapter focuses only on the latter part, where we actually look through a collection of items (for example, an index) in order to find a match.

This means that we will compare items directly (a == b) to determine closeness, rather than using something such as a distance - or locally-sensitive hashing function. These can be found in more specific domains such as a fuzzy search or matching...

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