Arrogante Arbeitskollegin ausmanövriert und abgerichtet #1- Beförderung und Degradierung

Conversion from EPUBDive Into Algorithms is a wide-ranging, Pythonic tour of many of the world's most interesting algorithms. With little more than a bit of computer programming experience and basic high-school math, you'll explore standard computer science algorithms for searching, sorting, and optimization; human-based algorithms that help us determine how to catch a baseball or eat the right amount at a buffet; and advanced algorithms like ones used in machine learning and artificial intelligence. You'll even explore how ancient Egyptians and Russian peasants used algorithms to multiply numbers, how the ancient Greeks used them to find greatest common divisors, and how Japanese scholars in the age of samurai designed algorithms capable of generating magic squares.You'll explore algorithms that are useful in pure mathematics and learn how mathematical ideas can improve algorithms. You'll learn about an algorithm for generating continued fractions, one for quick calculations of square roots, and another for generating seemingly random sets of numbers.You'll also learn how to:Use algorithms to debug code, maximize revenue, schedule tasks, and create decision treesMeasure the efficiency and speed of algorithmsGenerate Voronoi diagrams for use in various geometric applicationsUse algorithms to build a simple chatbot, win at board games, or solve sudoku puzzlesWrite code for gradient ascent and descent algorithms that can find the maxima and minima of functionsUse simulated annealing to perform global optimizationBuild a decision tree to predict happiness based on a person's characteristicsOnce you've finished this book you'll understand how to code and implement important algorithms as well as how to measure and optimize their performance, all while learning the nitty-gritty details of today's most powerful algorithms. Titlepage 10 Copyright 11 Dedication 13 About the Author 14 About the Technical Reviewer 14 Acknowledgments 15 Introduction 17 Who Is This Book For? 20 About This Book 21 Setting Up the Environment 24 Install Python on Windows 24 Install Python on macOS 25 Install Python on Linux 26 Installing Third-Party Modules 28 Summary 28 Chapter 1: Problem-Solving With Algorithms 29 The Analytic Approach 31 The Galilean Model 31 The Solve-for-x Strategy 34 The Inner Physicist 37 The Algorithmic Approach 39 Thinking with Your Neck 39 Applying Chapman’s Algorithm 44 Solving Problems with Algorithms 47 Summary 50 Chapter 2: Algorithms in History 51 Russian Peasant Multiplication 52 Doing RPM by Hand 53 Implementing RPM in Python 62 Euclid’s Algorithm 67 Doing Euclid’s Algorithm by Hand 67 Implementing Euclid’s Algorithm in Python 69 Japanese Magic Squares 71 Creating the Luo Shu Square in Python 72 Implementing Kurushima's Algorithm in Python 74 Summary 94 Chapter 3: Maximizing and Minimizing 96 Setting Tax Rates 97 Steps in the Right Direction 98 Turning the Steps into an Algorithm 104 Objections to Gradient Ascent 107 The Problem of Local Extrema 110 Education and Lifetime Income 110 Climbing the Education Hill—the Right Way 113 From Maximization to Minimization 117 Hill Climbing in General 120 When Not to Use an Algorithm 122 Summary 125 Chapter 4: Sorting and Searching 126 Insertion Sort 127 Putting the Insertion in Insertion Sort 128 Sorting via Insertion 132 Measuring Algorithm Efficiency 135 Why Aim for Efficiency? 136 Measuring Time Precisely 137 Counting Steps 139 Comparing to Well-Known Functions 143 Adding Even More Theoretical Precision 148 Using Big O Notation 151 Merge Sort 153 Merging 154 From Merging to Sorting 158 Sleep Sort 163 From Sorting to Searching 167 Binary Search 167 Applications of Binary Search 172 Summary 173 Chapter 5: Pure Math 175 Continued Fractions 176 Compressing and Communicating Phi 177 More about Continued Fractions 180 An Algorithm for Generating Continued Fractions 183 From Decimals to Continued Fractions 190 From Fractions to Radicals 195 Square Roots 196 The Babylonian Algorithm 196 Square Roots in Python 199 Random Number Generators 200 The Possibility of Randomness 201 Linear Congruential Generators 203 Judging a PRNG 206 The Diehard Tests for Randomness 209 Linear Feedback Shift Registers 212 Summary 217 Chapter 6: Advanced Optimization 219 Life of a Salesman 220 Setting Up the Problem 222 Brains vs. Brawn 229 The Nearest Neighbor Algorithm 232 Implementing Nearest Neighbor Search 233 Checking for Further Improvements 236 Algorithms for the Avaricious 240 Introducing the Temperature Function 242 Simulated Annealing 246 Tuning Our Algorithm 250 Avoiding Major Setbacks 254 Allowing Resets 256 Testing Our Performance 258 Summary 261 Chapter 7: Geometry 263 The Postmaster Problem 264 Triangles 101 268 Advanced Graduate-Level Triangle Studies 272 Finding the Circumcenter 273 Increasing Our Plotting Capabilities 277 Delaunay Triangulation 279 Incrementally Generating Delaunay Triangulations 282 Implementing Delaunay Triangulations 288 From Delaunay to Voronoi 296 Summary 304 Chapter 8: Language 306 Why Language Algorithms Are Hard 307 Space Insertion 308 Defining a Word List and Finding Words 310 Dealing with Compound Words 313 Checking Between Existing Spaces for Potential Words 314 Using an Imported Corpus to Check for Valid Words 317 Finding First and Second Halves of Potential Words 320 Phrase Completion 326 Tokenizing and Getting N-grams 326 Our Strategy 329 Finding Candidate n + 1-grams 330 Selecting a Phrase Based on Frequency 332 Summary 335 Chapter 9: Machine Learning 337 Decision Trees 338 Building a Decision Tree 341 Downloading Our Dataset 342 Looking at the Data 343 Splitting Our Data 346 Smarter Splitting 349 Choosing Splitting Variables 353 Adding Depth 357 Evaluating Our Decision Tree 362 The Problem of Overfitting 364 Improvements and Refinements 369 Random Forests 371 Summary 373 Chapter 10: Artificial Intelligence 374 La Pipopipette 376 Drawing the Board 378 Representing Games 379 Scoring Games 382 Game Trees and How to Win a Game 384 Building Our Tree 387 Winning a Game 393 Adding Enhancements 400 Summary 402 Chapter 11: Forging Ahead 403 Doing More with Algorithms 404 Building a Chatbot 406 Text Vectorization 410 Vector Similarity 414 Becoming Better and Faster 419 Algorithms for the Ambitious 420 Solving the Deepest Mysteries 425 Index 430 Dive Into Algorithms is a broad introduction to algorithms using the Python Programming Language.Dive Into Algorithms is a wide-ranging, Pythonic tour of many of the world's most interesting algorithms. With little more than a bit of computer programming experience and basic high-school math, you'll explore standard computer science algorithms for searching, sorting, and optimization; human-based algorithms that help us determine how to catch a baseball or eat the right amount at a buffet; and advanced algorithms like ones used in machine learning and artificial intelligence. You'll even explore how ancient Egyptians and Russian peasants used algorithms to multiply numbers, how the ancient Greeks used them to find greatest common divisors, and how Japanese scholars in the age of samurai designed algorithms capable of generating magic squares.You'll explore algorithms that are useful in pure mathematics and learn how mathematical ideas can improve algorithms. You'll learn about an algorithm for generating continued fractions, one for quick calculations of square roots, and another for generating seemingly random sets of numbers.You'll also learn how to: • Use algorithms to debug code, maximize revenue, schedule tasks, and create decision trees • Measure the efficiency and speed of algorithms • Generate Voronoi diagrams for use in various geometric applications • Use algorithms to build a simple chatbot, win at board games, or solve sudoku puzzles • Write code for gradient ascent and descent algorithms that can find the maxima and minima of functions • Use simulated annealing to perform global optimization • Build a decision tree to predict happiness based on a person's characteristicsOnce you've finished this book you'll understand how to code and implement important algorithms as well as how to measure and optimize their performance, all while learning the nitty-gritty details of today's most powerful algorithms. A Fun Yet Thorough Python-based Introduction To Algorithms, Which Are Sets Of Instructions That Allow A Computer To Solve Problems. The Book Tackles Classic Algorithms Like Searching, Sorting, And Optimization As Well As Those Used In Fields Like Machine Learning And Artificial Intelligence. Algorithms For The Adventurous Is A Thorough Introduction To Algorithms, Which Are Sets Of Instructions That Allow A Computer To Solve A Problem And Are Key To The Success Of Many Of Today's Computer Applications. Readers Learn About Many Standard Computer Science Algorithms Including Ones For Searching, Sorting, And Optimization As Well As Newer Ones Used In Machine Learning And Artificial Intelligence. Readers Also Learn How To Understand Real Life Algorithms Like How A Baseball Outfielder Uses An Algorithm To Determine Where To Run To Field A Ball; How Computers Can Beat Humans At Games Like Chess; How A Chatbot Can Understand And Respond To Human Speech; And How Algorithms Have Been Used Throughout History. Readers Need Little More Than High School Math To Understand An Algorithm And The Python Code Needed To Implement The Algorithm -- All Of Which Is Introduced Line-by-line In Order To Make The Code As Understandable As Possible. A fun yet thorough Python-based introduction to algorithms, which are sets of instructions that allow a computer to solve problems. The book tackles classic algorithms like searching, sorting, and optimization as well as those used in fields like machine learning and artificial intelligence. Dive Into Algorithms is a thorough introduction to algorithms, which are sets of instructions that allow a computer to solve a problem and are key to the success of many of today's computer applications. Readers learn about many standard computer science algorithms including ones for searching, sorting, and optimization as well as newer ones used in machine learning and artificial intelligence. Readers also learn how to understand "real life" algorithms like how a baseball outfielder uses an algorithm to determine where to run to field a ball; how computers can beat humans at games like chess; how a chatbot can understand and respond to human speech; and how algorithms have been used throughout history. Readers need little more than high school math to understand an algorithm and the Python code needed to implement the algorithm -- all of which is introduced line-by-line in order to make the code as understandable as possible. "An introduction to algorithms for beginning coders that teaches readers a variety of common computer science algorithms, such as searching and sorting and optimization, as well as newer algorithms like those used in machine learning"-- Provided by publisher