Algorithms: 3 books in 1: Practical Guide to Learn Algorithms For Beginners + Design Algorithms to Solve Common Problems + Advanced Data Structures for Algorithms
Have you ever wondered how a programmer develops games and writes code without having to think too much? Do you want to know what makes a programmer confident about the code they write? Do you want to learn how programmers use algorithms to determine how to structure their programs before they develop it? If you did, this is the book for you.
An algorithm is a set of rules or instructions you provide to a system. The system performs a specific process to answer a question using these instructions. As an amateur or expert, it is important for you to understand what an algorithm is and how you should define it. Once you learn to develop an algorithm, you can easily learn to develop code to execute that algorithm. Throughout this book, you will learn about the following:
• Algorithms and their characteristics
• How you should define the algorithm
• Types of algorithms
• Analyzing an algorithm based on time and space complexities
• Writing code keeping an algorithm in mind
• And more!
This book will also show you how to implement the sorting and searching algorithms using C and Java programming languages, since these are the most common languages used by programmers. Bear in mind an algorithm is only a set of instructions, and this means you can use an algorithm to write code using different programming languages. You will only need to understand the properties of the programming language and use the right syntax.
Are you interested in furthering your knowledge of algorithms ? Do you want to learn how they work for real-world problems? Then you’ve come to the right place. This guide will walk you through algorithm design before digging into some of the top design techniques.
Here’s what you will learn:
• The steps involved in designing an algorithm
• The top algorithm design techniques
• The Divide and Conquer algorithm
• The Greedy Algorithm
• Dynamic Programming
• The Branch and Bound Algorithm
• The Randomized Algorithm
• Recursion and backtracking
And everything that goes with them.
Included are plenty of algorithm designs and code implementations to show you how it all works.
Are you studying data science and want to take your learning further ? Data structures are an integral part of data science, machine learning, and algorithms, all aimed at solving programming challenges that might seem insurmountable at the outset. Advanced Data Structures for Algorithms builds on your current knowledge, taking your learning much deeper and teaching you how to solve even the trickiest of challenges.
This book has been divided into four parts:
Part One covers advanced lists, including:
• An overview of linked lists
• Doubly linked lists
• XOR linked lists
• Self-organizing lists
• Unrolled linked lists
Part Two covers trees, including:
• Segment trees
• Trie trees
• Fenwick trees
• AVL trees
• Red-black trees
• Scapegoat trees
Part Three discusses disjoint sets or Union-finds, as they are sometimes known
Part Four covers heaps and priority queues, including:
• A brief discussion on binary heaps
• Binomial heaps
• Fibonacci heaps
• Leftist heaps
• K-ary heaps
• Iterative heapsorts
You’ll find plenty of code examples to help you make sense of things and common-sense explanations.
If you want to advance your knowledge of data structures for algorithms you are in the right place.
ALGORITHMSPractical Guide to Learn Algorithms for Beginners ALGORITHMSPractical Guide to Learn Algorithms for Beginners Introduction Chapter One: Introduction to Algorithms Chapter One: Introduction to Algorithms Association between Algorithms and Computer Science Characteristics of an Algorithm Designing an Algorithm How to Identify the Best Algorithm Understanding the Basic Algorithm that Digitally Powers Life Benefits of Algorithmic Thinking Pros and Cons Chapter Two: Types of Algorithms Chapter Two: Types of Algorithms Backtracking Algorithm Brute Force Algorithm Divide and Conquer Algorithm Dynamic Programming Algorithm Greedy Algorithm Randomized Algorithm Simple Recursive Algorithm Chapter Three: Describing Algorithms Chapter Three: Describing Algorithms Chapter Four: Error Handling Chapter Four: Error Handling Checking for Exceptions Defining Exceptions Special Case Patterns Nulls Common Error Messages Dealing with errors Chapter Five: Analysis of Algorithms Chapter Five: Analysis of Algorithms Importance of Analysis Analysis Methods Space Complexities Understanding Recursion Chapter Six: An Introduction to Writing Programs Chapter Six: An Introduction to Writing Programs Principles of Programming Objects and Classes Data Types Operations Chapter Seven: Types of Programming Languages Chapter Seven: Types of Programming Languages Definition Types of Programming Languages Programming Languages Chapter Eight: Important Programming Techniques Chapter Eight: Important Programming Techniques Arrays Building Big Programs Bitwise Logic Boolean Logic Closures Concurrency Decision or Selection Disk Access Immutability Interacting with the Command Line Interacting with the OS Lambdas Loops and Repetitions Linked Lists Modular Arithmetic Pointers Safe Calls Scaling and Random Numbers Strings Structures Text Manipulation Trigonometry Variables Chapter Nine: Testing the Program Chapter Nine: Testing the Program Laws of TTD Keeping the Tests Clean Testing the Abilities of the Code Clean Tests Characteristics of Tests Chapter Ten: Sorting and Searching Algorithms Chapter Ten: Sorting and Searching Algorithms Searching Algorithms Sorting Algorithms Chapter Eleven: Loop Control and Decision Making Chapter Eleven: Loop Control and Decision Making Decision Making Loop Statements Loop Control Statements Chapter Twelve: Introduction to Data Structures Chapter Twelve: Introduction to Data Structures The Struct Statement Accessing Structure Members Using Structures as Arguments Using Pointers in Structures Typedef Keyword Chapter Thirteen: Comments and Formatting Chapter Thirteen: Comments and Formatting Comments Formatting Chapter Fourteen: Debugging Chapter Fourteen: Debugging Conclusion Resources ALGORITHMSDesign Algorithms to Solve Common Problems ALGORITHMSDesign Algorithms to Solve Common Problems Introduction Chapter 1: Designing an Algorithm Chapter 1: Designing an Algorithm Designing an Algorithm Algorithm Design Techniques Chapter 2: Divide and Conquer Chapter 2: Divide and Conquer Quicksort Mergesort Closest Pair of Points Chapter 3: Greedy Algorithms Chapter 3: Greedy Algorithms Creating a Greedy Algorithm Greedy Algorithms Examples Graph Coloring Greedy Algorithm Building a Huffman Tree Chapter 4: Dynamic Programming Chapter 4: Dynamic Programming Subproblems Memoization with Fibonacci Numbers Dynamic Programming Process Paradox of Choice: Multiple Options Dynamic Programming Runtime Analysis of Dynamic Programs Dynamic Programming Algorithms Bellman-Ford Algorithm Chapter 5: Branch and Bound Chapter 5: Branch and Bound Knapsack Using Branch and Bound Branch and Bound Using Branch and Bound to Generate Binary Strings of Length N Chapter 6: Randomized Algorithm Chapter 6: Randomized Algorithm Conditional Probability Random Variables How to Analyze Randomized Algorithms Randomized Algorithms – Classification and Application Chapter 7: Recursion and Backtracking Chapter 7: Recursion and Backtracking Recursion Backtracking The Knight's Tour Problem Subset Sum Conclusion Resources AlgorithmsAdvanced Data Structures for Algorithms AlgorithmsAdvanced Data Structures for Algorithms Introduction Part 1: Advanced Lists Part 1: Advanced Lists Linked Lists Doubly Linked List XOR Linked Lists Self-Organizing Lists Unrolled Linked List Part 2: Advanced Trees Part 2: Advanced Trees Segment Trees Trie Data Structures Fenwick Tree AVL Tree Red Black Tree Scapegoat Trees Treap N-ary Tree Part 3: Disjoint Sets Part 3: Disjoint Sets Disjoint Set Data Structures Part 4: Advanced Heaps and Priority Queues Part 4: Advanced Heaps and Priority Queues Binary Heap or Binary Search Tree for Priority Queues? Binomial Heap Fibonacci Heap Leftist Heap K-ary Heap Heapsort/Iterative HeapSort Conclusion References
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