Textbook in PDF format

Схема алгоритма Гроккинга: Расширенное руководство, которое поможет вам преуспеть в использовании алгоритмов Гроккинга
Grokking Algorithms is a book that aims to help readers understand the fundamental concepts of Computer Science algorithms. It covers various algorithms and data structures, including searching, sorting, graph algorithms, dynamic programming, and more.
The book presents the concepts clearly and concisely, with plenty of illustrations and examples to help readers grasp the ideas. It also includes exercises and challenges at the end of each chapter to help readers test their understanding and apply their knowledge.
An algorithm is a set of steps or procedures to solve a problem or perform a task. Algorithms are used to perform calculations, data processing, and automated reasoning tasks. They are an essential part of Computer Science and are used in various applications, including search engines, image recognition, and Machine Learning.
An algorithm is typically designed to take some input data, perform a series of operations on the data, and produce an output. An algorithm’s steps are usually defined in clearly and precisely, and the algorithm is designed to terminate after a finite number of steps. Algorithms are typically implemented in programming languages and run on computers or other devices.
Grokking Algorithms aims to give readers a solid foundation in Computer Science algorithms and to help them develop the skills and confidence to tackle complex problems using algorithms. It is an excellent resource for Computer Science students, software engineers, and anyone interested in learning algorithms and data structures.
Introduction
Chapter Introduction to Grokking Algorithms
Why Are Grokking Algorithms Important?
Chapter Time and Space Complexity
Time Complexity
Space Complexity
Time and Space Complexity in Practice
Understanding the Big O Notation
Analyzing the Time and Space Complexity of Different Algorithms
Chapter Sorting Algorithms
Types of Sorting Algorithms
Chapter Searching Algorithms Types of Search Algorithms Linear Search Binary Search Hash Tables and Their Use in Searching
Chapter Dynamic Programming Other Common Dynamic Programming Problems and Solutions
Chapter Graph Algorithms Introduction to Graph Theory Breadth-First Search and Depth-First Search
Shortest Path Algorithms: Dijkstra’s and A* Minimum Spanning Tree Algorithms: Kruskal and Prim Chapter Recursion Examples of Recursive Algorithms Tail Recursion and Optimization Chapter Divide and Conquer The Merge Sort Algorithm as an Example of Divide and Conquer Chapter Greedy Algorithms Key Characteristics of Greedy Algorithms The Process of Constructing a Greedy Algorithm The Role of Optimization in Greedy Algorithms Common Pitfalls to avoid When using Greedy Algorithms The Role of Mathematical Proof in Greedy Algorithms Techniques for Proving the Correctness of Greedy Algorithms The Knapsack Problem as an Example of a Greedy Algorithm The Huffman Coding Algorithm Chapter Backtracking The N-Queens Problem as an Example of Backtracking Chapter Conclusion and Next Steps Tips for Continuing to Improve Your Skills with Algorithms Resources for Further Learning and Practice Ideas for Incorporating Algorithms into Your Daily Work and Projects
The Importance of Staying Up-to-Date with New Developments and Advancements in the Field of Algorithms. Common Algorithms with Their Coding Conclusion