Introduction to Algorithms : A Comprehensive Guide for Beginners / Cuantum Technologies LLC, issuing body.

Format:

Book

Author/Creator:

LLC, Cuantum Technologies, Author.

Contributor:

Cuantum Technologies LLC, issuing body.

Language:

English

Subjects (All):

Algorithms.

Computer algorithms.

Computer programming.

Physical Description:

1 online resource (294 pages)

Edition:

First edition.

Place of Publication:

Plano, TX : Cuantum Technologies LLC, [2023]

Biography/History:

LLC Cuantum Technologies: Cuantum Technologies is a leading innovator in the realm of software development and education, with a special focus on leveraging the power of Artificial Intelligence and cutting-edge technology. They specialize in web-based software development, authoring insightful programming and AI literature, and building captivating web experiences with the intricate use of HTML, CSS, JavaScript, and Three. js. Their diverse array of products includes CuantumAI, a pioneering SaaS offering, and an array of books spanning from Python, NLP, PHP, JavaScript, and beyond. Through their services, they are constantly striving to demystify AI and technology, making it accessible, understandable, and useable for all.

Summary:

Discover the fundamentals and advanced concepts of algorithms with this comprehensive course. Learn about efficiency, types, design techniques, and real-world applications, and enhance your algorithmic knowledge.Key FeaturesBasics to advanced algorithm design and applications, along with real-world applicationsEngaging exercises & case studies from the latest industry trends & practices for reinforcementClear, step-by-step instructions for complex and advanced topicsBook DescriptionBegin your journey into the fascinating world of algorithms with this comprehensive course. Starting with an introduction to the basics, you will learn about pseudocode and flowcharts, the fundamental tools for representing algorithms. As you progress, you'll delve into the efficiency of algorithms, understanding how to evaluate and optimize them for better performance. The course will also cover various basic algorithm types, providing a solid foundation for further exploration. You will explore specific categories of algorithms, including search and sort algorithms, which are crucial for managing and retrieving data efficiently. You will also learn about graph algorithms, which are essential for solving problems related to networks and relationships. Additionally, the course will introduce you to the data structures commonly used in algorithms. Towards the end, the focus shifts to algorithm design techniques and their real-world applications. You will discover various strategies for creating efficient and effective algorithms and see how these techniques are applied in real-world scenarios. By the end of the course, you will have a thorough understanding of algorithmic principles and be equipped with the skills to apply them in your technical career.What you will learnUnderstand the basics of algorithms and their significanceEvaluate the efficiency of different algorithmsApply various types of algorithms to solve complex problemsUtilize graph algorithms for network-related issuesImplement appropriate data structures for algorithm optimizationDesign efficient algorithms for real-world applicationsWho this book is forThis course is designed for a wide range of learners, including technical professionals looking to enhance their algorithmic knowledge, computer science students seeking a deeper understanding of algorithm principles, and software developers aiming to improve their coding efficiency. Additionally, it is suitable for data scientists and analysts who need to apply algorithms to data management and analysis tasks, educators looking for comprehensive teaching material on algorithms, and hobbyists interested in expanding their technical skill set.

Contents:

Intro

Code Blocks Resource

Premium Customer Support

Who we are

Our Philosophy

Our Expertise

Introduction

Preface

Introduction to the Book

Who Should Read This Book

How to Use This Book

Chapter 1: Introduction to Algorithms

1.1 What is an Algorithm?

1.1.1 Characteristics of a Good Algorithm

1.1.2 How Algorithms are Used

1.1.3 Brief Summary and Some Points for Further Reflection

1.2 Importance of Algorithms in Computer Science

1.2.1 Algorithms Power Our Digital World

1.2.2 Algorithms Drive Efficiency

1.2.3 Algorithms Form the Basis of Advanced Fields

1.2.4 The Future Implications and Advancements in Algorithms

1.3 Fundamentals of Computational Thinking

1.3.1 Decomposition

1.3.2 Pattern Recognition

1.3.3 Abstraction

1.3.4 Algorithmic Thinking

1.3.5 Debugging and Iteration

1.4 Practice Problems

Problem 1: Password Generator

Problem 2: Calendar Events

Problem 3: Building a Pyramid

Problem 4: Text Compression

1.5 Chapter Summary

Chapter 2: Pseudocode and Flowcharts

2.1 Understanding Pseudocode

2.1.1 Flexibility of Pseudocode

2.2 Understanding Flowcharts

2.3 Translating Real-World Problems into Pseudocode

2.4 Practice Problems

Problem 1

Problem 2

Problem 3

Chapter 2 Summary

Chapter 3: Algorithm Efficiency

3.1 Understanding Time Complexity

3.1.1 Big O notation

3.1.2 Difference between the best-case, average-case, and worst-case time complexity

3.2 Understanding Space Complexity

3.2.1 Caching/Memoization

3.3 Introduction to Big O Notation

3.3.1 What is Big O Notation?

3.3.2 Common Types of Time Complexities

3.3.3 Asymptotic Analysis

3.4 Practice Problems

Linear Search:

Sum of Elements:

Find Duplicate:

Bubble Sort:

Chapter 3 Summary

Chapter 4: Basic Algorithm Types.

4.1 Divide and Conquer Algorithms

4.2 Greedy Algorithms

4.2.1 What is a Greedy Algorithm?

4.2.2 Coin Change Problem

4.3 Dynamic Programming Algorithms

4.4 Recursive Algorithms

4.4.1 Tail Recursion

4.5 Practice Problems

Problem 1: Binary Search (Divide and Conquer)

Problem 2: Coin Change (Greedy Algorithm)

Problem 3: Fibonacci Series (Dynamic Programming)

Problem 4: Sum of Natural Numbers (Recursive Algorithm)

Problem 5: QuickSort (Divide and Conquer)

Problem 6: Implementing a Stack using Recursion (Recursive Algorithm)

Chapter 4 Summary

Chapter 5: Search Algorithms

5.1 Linear Search

5.1.1 Limitations of Linear Search

5.2: Binary Search

5.3 Hashing and Hash Tables

5.3.1 Collisions

5.4 Practice Problems

Problem 1: Linear Search

Problem 2: Binary Search

Problem 3: Hashing

Problem 4: Binary Search vs Linear Search

Chapter 5 Summary

Chapter 6: Sort Algorithms

6.1 Bubble Sort

6.1.1 When and Why to Use or Not to Use Bubble Sort

6.2 Selection Sort

6.3 Insertion Sort

6.4 Quick Sort

6.5 Merge Sort

6.6 Heap Sort

6.7 Practice Problems

1. Implement Bubble Sort

2. Analyze Quick Sort's Worst Case

3. Merge Sort with Linked Lists

4. Heapify an Array

5. Stability in Sorting

Chapter 6 Summary

Chapter 7: Graph Algorithms

7.1 Introduction to Graph Theory

7.2 Depth-First Search

7.3 Breadth-First Search

7.3.1 BFS Time Complexity

7.4 Dijkstra's Algorithm

7.5 A* Search

7.5.1 Heuristics in A*

7.5.2 Time and Space Complexity

7.5.3 Optimality of A Search

7.5.4 Real-world Applications of A Search*

7.5.5 Variations of A Search*

7.5.6 Complexity of A Search*

7.6 Practice Problems

Problem 1: DFS in Maze Solving

Problem 2: Shortest Path in a Grid with BFS

Chapter 7 Summary.

Chapter 8: Data Structures Used in Algorithms

8.1 Arrays

8.1.1 Properties and Common Uses of Arrays

8.2 Linked Lists

8.2.1 Other Types of Linked Lists

8.3 Stacks and Queues

8.3.1. Stacks

8.3.2 Queues

8.3.3 Priority Queues and Dequeues

8.4 Trees and Graphs

8.4.1 Trees

8.4.2 Graphs

8.4.3 Going Deeper

8.5 Practice Problems

Problem 1: Arrays - Maximum Subarray Sum

Problem 2: Linked Lists - Reverse a Linked List

Problem 3: Stacks - Valid Parentheses

Problem 4: Trees - Maximum Depth of Binary Tree

Chapter 8 Summary

Chapter 9: Algorithm Design Techniques

9.1 Recursion

9.2 Iterative Approaches

9.2.1 Iterative Factorial Calculation

9.2.2 The Tail Recursion Optimization

9.3 Backtracking

9.4 Branch and Bound

9.4.1 Working Principle of Branch and Bound

9.4.2 The Travelling Salesman Problem

9.5 Practical Problems

1. Recursion: Fibonacci Series

2. Iterative Approach: Factorial

3. Backtracking: N-Queens Problem

4. Branch and Bound: Travelling Salesman Problem

Chapter 9 Summary

Chapter 10: Real World Applications of Algorithms

10.1 Algorithms in Databases

10.2 Algorithms in Artificial Intelligence

10.2.1 Machine Learning Algorithms

10.2.2 Search Algorithms

10.2.3 How Algorithms Can Power NLP in AI

10.2.4 Role of Algorithms in Machine Learning

10.3 Algorithms in Network Routing

10.3.1 Dijkstra's Algorithm

10.3.2 Bellman-Ford Algorithm

10.3.3 Link State Routing Protocol (LSRP)

10.4 Practice Problems

Problem 1: Algorithms in Databases

Problem 2: Algorithms in Artificial Intelligence

Problem 3: Algorithms in Network Routing

Chapter 10 Summary

Conclusion

Where to continue?

Know more about us

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Notes:

Description based on publisher supplied metadata and other sources.

Description based on print version record.

Other Format:

Print version: LLC, Cuantum Technologies Introduction to Algorithms

ISBN:

9781836203865

OCLC:

1463579978