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Practical discrete mathematics : discover math principles that fuel algorithms for computer science and machine learning with python / Ray T. White and Archana Tikayat Ray

By: Contributor(s): Material type: TextTextPublication details: Mumbai : Packt Publishing, c2021Description: 309 pages : illustration ; 24cmISBN:
  • 978-1-83898-314-7
Subject(s): LOC classification:
  • GC QA 39.3 .W45 2021 c.1
Contents:
Part I. Basic Concepts of DIscrete math -- 1. Key Concepts, Notation, Set Theory, Relations, and Functions -- 2. Formal Logic and Construdcting Mathematical Proofs -- 3. Computing with Base-n Numbers -- 4. Combinatorics Using SciPy -- 5. Elements of Discrete Probability -- Part II. Implementing Discrete Mathematics in Data and Computer Science -- 6. Computational Algorithms in Linear Algebra -- 7. Computational Requirements for Algorithms -- 8. Storage and Feature Extraction of Graphs, Trees, and Networks -- 9. Searching Data Structures and Finding Shortest Paths -- Part III - Real - World Applications of Discrete Mathematics -- 10. Regression Analysis with NumPy and Scikit-Learn -- 11. Web Searches with PageRank -- 12. Principal Component Analysis with Scikit-Learn
Summary: Discrete mathematics deals with studying countable, distinct elements, and its principles are widely used in building lagorithms for computer science and dat science. The knowledge of discrete math concepts will help you understand the alghorithms, binary, and general mathematics that sit at the core of data-driven task. Prictical Discret Mathamatics is comprehensive instroduction for those who are new to the mathamatics of countable object. THis book will help you up to speed with using discret math principles to take your computer science skills to a more advanced level. As you learn the language of discrete mathamaticsm you'll alaso cover methods crucial to studing and descrbing computer science skills to a more advanc ed level. As you learn the lanuage of discret mathematics. you'll also cover methods crusical to studing and describing computer science and machine learning objects and algorithms. The chapters that follow will guide you through how memory and CPUs work. In addition tho thi. you'll undertsand how to analyze data for useful patterns, before finally exploring how to apply math concepts in nterwork routing, web searching, data science.\
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Books Books NU BALIWAG NU BALIWAG General Circulation GC QA 39.3 .W45 2021 c.1 (Browse shelf(Opens below)) Available NUBUL000005078

Include index.

Part I. Basic Concepts of DIscrete math -- 1. Key Concepts, Notation, Set Theory, Relations, and Functions -- 2. Formal Logic and Construdcting Mathematical Proofs -- 3. Computing with Base-n Numbers -- 4. Combinatorics Using SciPy -- 5. Elements of Discrete Probability -- Part II. Implementing Discrete Mathematics in Data and Computer Science -- 6. Computational Algorithms in Linear Algebra -- 7. Computational Requirements for Algorithms -- 8. Storage and Feature Extraction of Graphs, Trees, and Networks -- 9. Searching Data Structures and Finding Shortest Paths -- Part III - Real - World Applications of Discrete Mathematics -- 10. Regression Analysis with NumPy and Scikit-Learn -- 11. Web Searches with PageRank -- 12. Principal Component Analysis with Scikit-Learn

Discrete mathematics deals with studying countable, distinct elements, and its principles are widely used in building lagorithms for computer science and dat science. The knowledge of discrete math concepts will help you understand the alghorithms, binary, and general mathematics that sit at the core of data-driven task. Prictical Discret Mathamatics is comprehensive instroduction for those who are new to the mathamatics of countable object. THis book will help you up to speed with using discret math principles to take your computer science skills to a more advanced level. As you learn the language of discrete mathamaticsm you'll alaso cover methods crucial to studing and descrbing computer science skills to a more advanc ed level. As you learn the lanuage of discret mathematics. you'll also cover methods crusical to studing and describing computer science and machine learning objects and algorithms. The chapters that follow will guide you through how memory and CPUs work. In addition tho thi. you'll undertsand how to analyze data for useful patterns, before finally exploring how to apply math concepts in nterwork routing, web searching, data science.\

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