Algorithms and data structures Books

Book SynopsisA riveting and revealing exploration of the world created by computer algorithms and its impact on individuals, from the workers across the globe who feed artificial intelligence systems with data to the impact of algorithms on our own behaviour, as consumers and citizens.

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Book SynopsisA fascinating exploration of how computer algorithms can be applied to our everyday lives.In this dazzlingly interdisciplinary work, acclaimed author Brian Christian and cognitive scientist Tom Griffiths show us how the simple, precise algorithms used by computers can also untangle very human questions. Modern life is constrained by limited space and time, limits that give rise to a particular set of problems. What should we do, or leave undone, in a day or a lifetime? How much messiness should we accept? The authors explain how to have better hunches and when to leave things to chance, how to deal with overwhelming choices and how best to connect with others.From finding a spouse to finding a parking spot, from organizing one''s inbox to understanding the workings of human memory, Algorithms To Live By is full of practical takeaways to help you solve common decision-making problems and illuminate the workings of the human mind.Trade Review‘I’ve been waiting for a book to come along that merges computational models with human psychology – and Christian and Griffiths have succeeded beyond all expectations. This is a wonderful book, written so that anyone can understand the computer science that runs our world – and more importantly, what it means to our lives’ David Eagleman, author of ‘Sum: Tales from the Afterlives’ ‘Compelling and entertaining, Algorithms to Live By is packed with practical advice about how to use time, space, and effort more efficiently. And it’s a fascinating exploration of the workings of computer science and the human mind. Whether you want to optimize your to-do list, organize your closet, or understand human memory, this is a great read’ ‘Charles Duhigg, author of The Power of Habit’ ‘A truly beautiful exploration through math, computer science and philosophy of some of the most ordinary, yet most important dilemmas any of us is likely to face. Filled with humour and wisdom, this is a bible with a brain’ Aarathi Prasad

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Book Synopsis Written for developers with some understanding of deep learning algorithms. Experience with reinforcement learning is not required. Grokking Deep Reinforcement Learning introduces this powerful machine learning approach, using examples, illustrations, exercises, and crystal-clear teaching. You'll love the perfectly paced teaching and the clever, engaging writing style as you dig into this awesome exploration of reinforcement learning fundamentals, effective deep learning techniques, and practical applications in this emerging field. We all learn through trial and error. We avoid the things that cause us to experience pain and failure. We embrace and build on the things that give us reward and success. This common pattern is the foundation of deep reinforcement learning: building machine learning systems that explore and learn based on the responses of the environment. • Foundational reinforcement learning concepts and methods • The most popular deep reinforcement learning agents solving high-dimensional environments • Cutting-edge agents that emulate human-like behavior and techniques for artificial general intelligence Deep reinforcement learning is a form of machine learning in which AI agents learn optimal behavior on their own from raw sensory input. The system perceives the environment, interprets the results of its past decisions and uses this information to optimize its behavior for maximum long-term return.

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Book SynopsisEvery other day we hear about new ways to put deep learning to good use: improved medical imaging, accurate credit card fraud detection, long range weather forecasting, and more. PyTorch puts these superpowers in your hands, providing a comfortable Python experience that gets you started quickly and then grows with you as you, and your deep learning skills, become more sophisticated. Deep Learning with PyTorch teaches you how to implement deep learning algorithms with Python and PyTorch. This book takes you into a fascinating case study: building an algorithm capable of detecting malignant lung tumors using CT scans. As the authors guide you through this real example, you'll discover just how effective and fun PyTorch can be. Key features • Using the PyTorch tensor API • Understanding automatic differentiation in PyTorch • Training deep neural networks • Monitoring training and visualizing results • Interoperability with NumPy Audience Written for developers with some knowledge of Python as well as basic linear algebra skills. Some understanding of deep learning will be helpful, however no experience with PyTorch or other deep learning frameworks is required. About the technology PyTorch is a machine learning framework with a strong focus on deep neural networks. Because it emphasizes GPU-based acceleration, PyTorch performs exceptionally well on readily-available hardware and scales easily to larger systems. Eli Stevens has worked in Silicon Valley for the past 15 years as a software engineer, and the past 7 years as Chief Technical Officer of a startup making medical device software. Luca Antiga is co-founder and CEO of an AI engineering company located in Bergamo, Italy, and a regular contributor to PyTorch.

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Book SynopsisShortlisted for the Women’s Prize for Non-Fiction 2024AI is changing what it means to be human. This is the unrivalled investigation into the impact of AI on how we live now.'The intimate investigation of AI that we’ve been waiting for, and it arrives not a moment too soon.' – Shoshana Zuboff, author of The Age of Surveillance CapitalismThrough the voices of ordinary people in places far removed from Silicon Valley, Code Dependent explores the impact of a set of powerful, flawed, and often exploitative technologies on individuals, communities, and our wider society. Madhumita Murgia, AI Editor at the FT, exposes how AI can strip away our collective and individual sense of agency – and shatter our illusion of free will.AI is already changing what it means to be human, in ways large and small. In this compelling work, Murgia reveals what could happen if we fail to reclaim our humanity.<

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Book SynopsisDESCRIPTION Being able to make near-real-time decisions is becoming increasingly crucial. To succeed, we need machine learning systems that can turn massive amounts of data into valuable insights. But when you're just starting out in the data science field, how do you get started creating machine learning applications? The answer is TensorFlow, a new open source machine learning library from Google. The TensorFlow library can take your high level designs and turn them into the low level mathematical operations required by machine learning algorithms. Machine Learning with TensorFlow teaches readers about machine learning algorithms and how to implement solutions with TensorFlow. It starts with an overview of machine learning concepts and moves on to the essentials needed to begin using TensorFlow. Each chapter zooms into a prominent example of machine learning. Readers can cover them all to master the basics or skip around to cater to their needs. By the end of this book, readers will be able to solve classification, clustering, regression, and prediction problems in the real world. KEY FEATURES • Lots of diagrams, code examples, and exercises • Solves real-world problems with TensorFlow • Uses well-studied neural network architectures • Presents code that can be used for the readers’ own applications AUDIENCE This book is for programmers who have some experience with Python and linear algebra concepts like vectors and matrices. No experience with machine learning is necessary. ABOUT THE TECHNOLOGY Google open-sourced their machine learning framework called TensorFlow in late 2015 under the Apache 2.0 license. Before that, it was used proprietarily by Google in its speech recognition, Search, Photos, and Gmail, among other applications. TensorFlow is one the most popular machine learning libraries.

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Book SynopsisAI is primed to revolutionize the way we build applications, offering exciting new ways to solve problems, uncover insights, innovate new products, and provide better user experiences. Successful AI is based on a set of core algorithms that form a base of knowledge shared by all data scientists. Grokking Artificial Intelligence Algorithms is a fully-illustrated and interactive tutorial guide to the different approaches and algorithms that underpin AI. Written in simple language and with lots of visual references and hands-on examples, readers learn the concepts, terminology, and theory they need to effectively incorporate AI algorithms into their applications. Grokking Artificial Intelligence Algorithms uses simple language, jargon-busting explanations, and hand-drawn diagrams to open up complex algorithms. Don’t worry if you aren’t a calculus wunderkind; you’ll need only the algebra you picked up in math class. • Use cases for different AI algorithms • How to encode problems and solutions using data structures • Intelligent search for game playing • Ant colony algorithms for path finding • Evolutionary algorithms for optimization problems For software developers with high school-level algebra and calculus skills.

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Book SynopsisDevelop a mathematical intuition around machine learning algorithms to improve model performance and effectively troubleshoot complex ML problems. For intermediate machine learning practitioners familiar with linear algebra, probability, and basic calculus. Machine Learning Algorithms in Depth dives into the design and underlying principles of some of the most exciting machine learning (ML) algorithms in the world today. With a particular emphasis on probability-based algorithms, you will learn the fundamentals of Bayesian inference and deep learning. You will also explore the core data structures and algorithmic paradigms for machine learning. You will explore practical implementations of dozens of ML algorithms, including: Monte Carlo Stock Price Simulation Image Denoising using Mean-Field Variational Inference EM algorithm for Hidden Markov Models Imbalanced Learning, Active Learning and Ensemble Learning Bayesian Optimisation for Hyperparameter Tuning Dirichlet Process K-Means for Clustering Applications Stock Clusters based on Inverse Covariance Estimation Energy Minimisation using Simulated Annealing Image Search based on ResNet Convolutional Neural Network Anomaly Detection in Time-Series using Variational Autoencoders Each algorithm is fully explored with both math and practical implementations so you can see how they work and put into action. About the technology Fully understanding how machine learning algorithms function is essential for any serious ML engineer. This vital knowledge lets you modify algorithms to your specific needs, understand the trade-offs when picking an algorithm for a project, and better interpret and explain your results to your stakeholders. This unique guide will take you from relying on one-size-fits-all ML libraries to developing your own algorithms to solve your business needs.

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Book SynopsisRobert Sedgewick has been a Professor of Computer Science at Princeton University since 1985, where he was the founding Chairman of the Department of Computer Science. He has held visiting research positions at Xerox PARC, Institute for Defense Analyses, and INRIA, and is member of the board of directors of Adobe Systems. Professor Sedgewick's research interests include analytic combinatorics, design and analysis of data structures and algorithms, and program visualization. His landmark book, Algorithms, now in its fourth edition, has appeared in numerous versions and languages over the past thirty years. In addition, with Kevin Wayne, he is the coauthor of the highly acclaimed textbook, Introduction to Programming in Java: An Interdisciplinary Approach (Addison-Wesley, 2008).   Kevin Wayne is the Phillip Y. Goldman Senior Lecturer in Computer Science at Princeton University, where hTable of Contents Chapter 1: Fundamentals 1.1 Programming Model 1.2 Data Abstraction 1.3 Queues, Stacks, and Bags 1.4 Analysis of Algorithms 1.5 Case Study: Union-Find Chapter 2: Sorting 2.1 Elementary Sorts 2.1 Elementary Sorts 2.2 Mergesort 2.3 Quicksort 2.4 Priority Queues 2.5 Applications Chapter 3: Searching 3.1 Symbol Tables 3.1 Symbol Tables 3.2 Binary Search Trees 3.3 Balanced Search Trees 3.4 Hash Tables 3.5 Applications Chapter 4: Graphs 4.1 Undirected graphs 4.1 Undirected graphs 4.2 Directed graphs 4.3 Minimum Spanning Trees 4.4 Shortest Paths Chapter 5: Strings 5.1 String Sorts 5.1 String Sorts 5.2 Tries 5.3 Substring Search 5.4 Regular Expressions 5.5 Data Compression Context Systems Programming Systems Programming Scientific Computing Commercial Applications Operations Research Intractability Index

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Book SynopsisDelve into the realm of generative AI and large language models (LLMs) while exploring modern deep learning techniques, including LSTMs, GRUs, RNNs with new chapters included in this 50% new edition overhaul Purchase of the print or Kindle book includes a free eBook in PDF format. Key Features Familiarize yourself with advanced deep learning architectures Explore newer topics, such as handling hidden bias in data and algorithm explainability Get to grips with different programming algorithms and choose the right data structures for their optimal implementation Book DescriptionThe ability to use algorithms to solve real-world problems is a must-have skill for any developer or programmer. This book will help you not only to develop the skills to select and use an algorithm to tackle problems in the real world but also to understand how it works. You'll start with an introduction to algorithms and discover various algorithm design techniques, before exploring how to implement different types of algorithms, with the help of practical examples. As you advance, you'll learn about linear programming, page ranking, and graphs, and will then work with machine learning algorithms to understand the math and logic behind them. Case studies will show you how to apply these algorithms optimally before you focus on deep learning algorithms and learn about different types of deep learning models along with their practical use. You will also learn about modern sequential models and their variants, algorithms, methodologies, and architectures that are used to implement Large Language Models (LLMs) such as ChatGPT. Finally, you'll become well versed in techniques that enable parallel processing, giving you the ability to use these algorithms for compute-intensive tasks. By the end of this programming book, you'll have become adept at solving real-world computational problems by using a wide range of algorithms.What you will learn Design algorithms for solving complex problems Become familiar with neural networks and deep learning techniques Explore existing data structures and algorithms found in Python libraries Implement graph algorithms for fraud detection using network analysis Delve into state-of-the-art algorithms for proficient Natural Language Processing illustrated with real-world examples Create a recommendation engine that suggests relevant movies to subscribers Grasp the concepts of sequential machine learning models and their foundational role in the development of cutting-edge LLMs Who this book is forThis computer science book is for programmers or developers who want to understand the use of algorithms for problem-solving and writing efficient code. Whether you are a beginner looking to learn the most used algorithms concisely or an experienced programmer looking to explore cutting-edge algorithms in data science, machine learning, and cryptography, you'll find this book useful. Python programming experience is a must, knowledge of data science will be helpful but not necessary.Table of ContentsTable of Contents Core Algorithms Data Structures Sorting and Searching Algorithms Designing Algorithms Graph Algorithms Unsupervised Machine Learning Algorithms Supervised Learning Algorithms Neural Network Algorithms Natural Language Processing Sequential Models Advanced Machine Learning Models Recommendation Engines Algorithmic Strategies for Data Handling Large-Scale Algorithms Evaluating Algorithmic Solutions Practical Considerations

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Book SynopsisHow populism is fueled by the demise of the industrial order and the emergence of a new digital society ruled by algorithmsIn the revolutionary excitement of the 1960s, young people around the world called for a radical shift away from the old industrial order, imagining a future of technological liberation and unfettered prosperity. IndustrialTrade Review"A welcome addition to the growing literature on the digital economy and change." * Choice *"Stimulating." * Paradigm Explorer *

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Book SynopsisA friendly introduction to the most usefulalgorithms written in simple, intuitive English The revised and updated second edition of Essential Algorithms, offers an accessible introduction to computer algorithms. The book contains a description of important classical algorithms and explains when each is appropriate. The author shows how to analyze algorithms in order to understand their behavior and teaches techniques that the can be used to create new algorithms to meet future needs. The text includes useful algorithms such as: methods for manipulating common data structures, advanced data structures, network algorithms, and numerical algorithms. It also offers a variety of general problem-solving techniques. In addition to describing algorithms and approaches, the author offers details on how to analyze the performance of algorithms. The book is filled with exercises that can be used to explore ways to modify the algorithms in order to apply them to new Table of ContentsIntroduction xxix Chapter 1 Algorithm Basics 1 Approach 2 Algorithms and Data Structures 2 Pseudocode 3 Algorithm Features 6 Big O Notation 7 Rule 1 8 Rule 2 8 Rule 3 9 Rule 4 9 Rule 5 10 Common Run Time Functions 11 1 11 Log N 11 Sqrt N 14 N 14 N log N 15 N2 15 2N 15 N! 16 Visualizing Functions 16 Practical Considerations 18 Summary 19 Exercises 20 Chapter 2 Numerical Algorithms 23 Randomizing Data 23 Generating Random Values 23 Generating Values 24 Ensuring Fairness 26 Getting Fairness from Biased Sources 28 Randomizing Arrays 29 Generating Nonuniform Distributions 30 Making Random Walks 31 Making Self-Avoiding Walks 33 Making Complete Self-Avoiding Walks 34 Finding Greatest Common Divisors 36 Calculating Greatest Common Divisors 36 Extending Greatest Common Divisors 38 Performing Exponentiation 40 Working with Prime Numbers 42 Finding Prime Factors 42 Finding Primes 44 Testing for Primality 45 Performing Numerical Integration 47 The Rectangle Rule 48 The Trapezoid Rule 49 Adaptive Quadrature 50 Monte Carlo Integration 54 Finding Zeros 55 Gaussian Elimination 57 Forward Elimination 58 Back Substitution 60 The Algorithm 61 Least Squares Fits 62 Linear Least Squares 62 Polynomial Least Squares 64 Summary 67 Exercises 68 Chapter 3 Linked Lists 71 Basic Concepts 71 Singly Linked Lists 72 Iterating Over the List 73 Finding Cells 73 Using Sentinels 74 Adding Cells at the Beginning 75 Adding Cells at the End 76 Inserting Cells After Other Cells 77 Deleting Cells 78 Doubly Linked Lists 79 Sorted Linked Lists 81 Self-Organizing Linked Lists 82 Move to Front (MTF) 83 Swap 83 Count 84 Hybrid Methods 84 Pseudocode 85 Linked-List Algorithms 86 Copying Lists 86 Sorting with Insertionsort 87 Sorting with Selectionsort 88 Multithreaded Linked Lists 90 Linked Lists with Loops 91 Marking Cells 92 Using Hash Tables 93 List Retracing 94 List Reversal 95 Tortoise and Hare 98 Loops in Doubly Linked Lists 100 Summary 100 Exercises 101 Chapter 4 Arrays 103 Basic Concepts 103 One-Dimensional Arrays 106 Finding Items 106 Finding Minimum, Maximum, and Average 107 Finding Median 108 Finding Mode 109 Inserting Items 112 Removing Items 113 Nonzero Lower Bounds 114 Two Dimensions 114 Higher Dimensions 115 Triangular Arrays 118 Sparse Arrays 121 Find a Row or Column 123 Get a Value 124 Set a Value 125 Delete a Value 127 Matrices 129 Summary 131 Exercises 132 Chapter 5 Stacks and Queues 135 Stacks 135 Linked-List Stacks 136 Array Stacks 138 Double Stacks 139 Stack Algorithms 141 Reversing an Array 141 Train Sorting 142 Tower of Hanoi 143 Stack Insertionsort 145 Stack Selectionsort 146 Queues 147 Linked-List Queues 148 Array Queues 148 Specialized Queues 151 Priority Queues 151 Deques 152 Binomial Heaps 152 Binomial Trees 152 Binomial Heaps 154 Merging Trees 155 Merging Heaps 156 Merging Tree Lists 156 Merging Trees 158 Enqueue 161 Dequeue 162 Runtime 163 Summary 163 Exercises 164 Chapter 6 Sorting 167 O(N2 ) Algorithms 168 Insertionsort in Arrays 168 Selectionsort in Arrays 170 Bubblesort 171 O(NlogN) Algorithms 174 Heapsort 175 Storing Complete Binary Trees 175 Defining Heaps 176 Implementing Heapsort 180 Quicksort 181 Analyzing Quicksort’s Run Time 182 Picking a Dividing Item 184 Implementing Quicksort with Stacks 185 Implementing Quicksort in Place 185 Using Quicksort 188 Mergesort 189 Sub O(NlogN) Algorithms 192 Countingsort 192 Pigeonhole Sort 193 Bucketsort 195 Summary 197 Exercises 198 Chapter 7 Searching 201 Linear Search 202 Binary Search 203 Interpolation Search 204 Majority Voting 205 Summary 207 Exercises 208 Chapter 8 Hash Tables 209 Hash Table Fundamentals 210 Chaining 211 Open Addressing 213 Removing Items 214 Linear Probing 215 Quadratic Probing 217 Pseudorandom Probing 219 Double Hashing 219 Ordered Hashing 219 Summary 222 Exercises 222 Chapter 9 Recursion 227 Basic Algorithms 228 Factorial 228 Fibonacci Numbers 230 Rod-Cutting 232 Brute Force 233 Recursion 233 Tower of Hanoi 235 Graphical Algorithms 238 Koch Curves 239 Hilbert Curve 241 Sierpiński Curve 243 Gaskets 246 The Skyline Problem 247 Lists 248 Divide and Conquer 249 Backtracking Algorithms 252 Eight Queens Problem 254 Knight’s Tour 257 Selections and Permutations 260 Selections with Loops 261 Selections with Duplicates 262 Selections without Duplicates 264 Permutations with Duplicates 265 Permutations without Duplicates 266 Round-Robin Scheduling 267 Odd Number of Teams 268 Even Number of Teams 270 Implementation 271 Recursion Removal 273 Tail Recursion Removal 274 Dynamic Programming 275 Bottom-Up Programming 277 General Recursion Removal 277 Summary 280 Exercises 281 Chapter 10 Trees 285 Tree Terminology 285 Binary Tree Properties 289 Tree Representations 292 Building Trees in General 292 Building Complete Trees 295 Tree Traversal 296 Preorder Traversal 297 Inorder Traversal 299 Postorder Traversal 300 Breadth-First Traversal 301 Traversal Uses 302 Traversal Run Times 303 Sorted Trees 303 Adding Nodes 303 Finding Nodes 306 Deleting Nodes 306 Lowest Common Ancestors 309 Sorted Trees 309 Parent Pointers 310 Parents and Depths 311 General Trees 312 Euler Tours 314 All Pairs 316 Threaded Trees 317 Building Threaded Trees 318 Using Threaded Trees 320 Specialized Tree Algorithms 322 The Animal Game 322 Expression Evaluation 324 Interval Trees 326 Building the Tree 328 Intersecting with Points 329 Intersecting with Intervals 330 Quadtrees 332 Adding Items 335 Finding Items 336 Tries 337 Adding Items 339 Finding Items 341 Summary 342 Exercises 342 Chapter 11 Balanced Trees 349 AVL Trees 350 Adding Values 350 Deleting Values 353 2-3 Trees 354 Adding Values 355 Deleting Values 356 B-Trees 359 Adding Values 360 Deleting Values 361 Balanced Tree Variations 362 Top-down B-trees 363 B+trees 363 Summary 365 Exercises 365 Chapter 12 Decision Trees 367 Searching Game Trees 368 Minimax 369 Initial Moves and Responses 373 Game Tree Heuristics 374 Searching General Decision Trees 375 Optimization Problems 376 Exhaustive Search 377 Branch and Bound 379 Decision Tree Heuristics 381 Random Search 381 Improving Paths 382 Simulated Annealing 384 Hill Climbing 385 Sorted Hill Climbing 386 Other Decision Tree Problems 387 Generalized Partition Problem 387 Subset Sum 388 Bin Packing 388 Cutting Stock 389 Knapsack 390 Traveling Salesman Problem 391 Satisfiability 391 Swarm Intelligence 392 Ant Colony Optimization 393 General Optimization 393 Traveling Salesman 393 Bees Algorithm 394 Swarm Simulation 394 Boids 395 Pseudoclassical Mechanics 396 Goals and Obstacles 397 Summary 397 Exercises 398 Chapter 13 Basic Network Algorithms 403 Network Terminology 403 Network Representations 407 Traversals 409 Depth-First Traversal 410 Breadth-First Traversal 412 Connectivity Testing 413 Spanning Trees 416 Minimal Spanning Trees 417 Euclidean Minimum Spanning Trees 418 Building Mazes 419 Strongly Connected Components 420 Kosaraju’s Algorithm 421 Algorithm Discussion 422 Finding Paths 425 Finding Any Path 425 Label-Setting Shortest Paths 426 Label-Correcting Shortest Paths 430 All-Pairs Shortest Paths 431 Transitivity 436 Transitive Closure 437 Transitive Reduction 438 Acyclic Networks 439 General Networks 440 Shortest Path Modifications 441 Shape Points 441 Early Stopping 442 Bidirectional Search 442 Best-First Search 442 Turn Penalties and Prohibitions 443 Geometric Calculations 443 Expanded Node Networks 444 Interchange Networks 445 Summary 447 Exercises 447 Chapter 14 More Network Algorithms 451 Topological Sorting 451 Cycle Detection 455 Map Coloring 456 Two-Coloring 456 Three-Coloring 458 Four-Coloring 459 Five-Coloring 459 Other Map-Coloring Algorithms 462 Maximal Flow 464 Work Assignment 467 Minimal Flow Cut 468 Network Cloning 470 Dictionaries 471 Clone References 472 Cliques 473 Brute Force 474 Bron–Kerbosch 475 Sets R, P, and X 475 Recursive Calls 476 Pseudocode 476 Example 477 Variations 480 Finding Triangles 480 Brute Force 481 Checking Local Links 481 Chiba and Nishizeki 482 Community Detection 483 Maximal Cliques 483 Girvan–Newman 483 Clique Percolation 485 Eulerian Paths and Cycles 485 Brute Force 486 Fleury’s Algorithm 486 Hierholzer’s Algorithm 487 Summary 488 Exercises 489 Chapter 15 String Algorithms 493 Matching Parentheses 494 Evaluating Arithmetic Expressions 495 Building Parse Trees 496 Pattern Matching 497 DFAs 497 Building DFAs for Regular Expressions 500 NFAs 502 String Searching 504 Calculating Edit Distance 508 Phonetic Algorithms 511 Soundex 511 Metaphone 513 Summary 514 Exercises 515 Chapter 16 Cryptography 519 Terminology 520 Transposition Ciphers 521 Row/Column Transposition 521 Column Transposition 523 Route Ciphers 525 Substitution Ciphers 526 Caesar Substitution 526 Vigenere Cipher 527 Simple Substitution 529 One-Time Pads 530 Block Ciphers 531 Substitution-Permutation Networks 531 Feistel Ciphers 533 Public-Key Encryption and RSA 534 Euler’s Totient Function 535 Multiplicative Inverses 536 An RSA Example 536 Practical Considerations 537 Other Uses for Cryptography 538 Summary 539 Exercises 540 Chapter 17 Complexity Theory 543 Notation 544 Complexity Classes 545 Reductions 548 3SAT 549 Bipartite Matching 550 NP-Hardness 550 Detection, Reporting, and Optimization Problems 551 Detection ≤p Reporting 552 Reporting ≤p Optimization 552 Reporting ≤p Detection 552 Optimization ≤p Reporting 553 Approximate Optimization 553 NP-Complete Problems 554 Summary 557 Exercises 558 Chapter 18 Distributed Algorithms 561 Types of Parallelism 562 Systolic Arrays 562 Distributed Computing 565 Multi-CPU Processing 567 Race Conditions 567 Deadlock 571 Quantum Computing 572 Distributed Algorithms 573 Debugging Distributed Algorithms 573 Embarrassingly Parallel Algorithms 574 Mergesort 576 Dining Philosophers 577 Randomization 578 Resource Hierarchy 578 Waiter 579 Chandy/Misra 579 The Two Generals Problem 580 Byzantine Generals 581 Consensus 584 Leader Election 587 Snapshot 588 Clock Synchronization 589 Summary 591 Exercises 591 Chapter 19 Interview Puzzles 595 Asking Interview Puzzle Questions 597 Answering Interview Puzzle Questions 598 Summary 602 Exercises 604 Appendix A Summary of Algorithmic Concepts 607 Chapter 1: Algorithm Basics 607 Chapter 2: Numeric Algorithms 608 Chapter 3: Linked Lists 609 Chapter 4: Arrays 610 Chapter 5: Stacks and Queues 610 Chapter 6: Sorting 610 Chapter 7: Searching 611 Chapter 8: Hash Tables 612 Chapter 9: Recursion 612 Chapter 10: Trees 614 Chapter 11: Balanced Trees 615 Chapter 12: Decision Trees 615 Chapter 13: Basic Network Algorithms 616 Chapter 14: More Network Algorithms 617 Chapter 15: String Algorithms 618 Chapter 16: Cryptography 618 Chapter 17: Complexity Theory 619 Chapter 18: Distributed Algorithms 620 Chapter 19: Interview Puzzles 621 Appendix B Solutions to Exercises 623 Chapter 1: Algorithm Basics 623 Chapter 2: Numerical Algorithms 626 Chapter 3: Linked Lists 633 Chapter 4: Arrays 638 Chapter 5: Stacks and Queues 648 Chapter 6: Sorting 650 Chapter 7: Searching 653 Chapter 8: Hash Tables 655 Chapter 9: Recursion 658 Chapter 10: Trees 663 Chapter 11: Balanced Trees 670 Chapter 12: Decision Trees 675 Chapter 13: Basic Network Algorithms 678 Chapter 14: More Network Algorithms 681 Chapter 15: String Algorithms 686 Chapter 16: Encryption 689 Chapter 17: Complexity Theory 692 Chapter 18: Distributed Algorithms 697 Chapter 19: Interview Puzzles 701 Glossary 711 Index 739

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Book SynopsisTable of ContentsIntroduction 1 Part 1: Getting Started with Algorithms 7 Chapter 1: Introducing Algorithms 9 Chapter 2: Considering Algorithm Design 23 Chapter 3: Working with Google Colab 41 Chapter 4: Performing Essential Data Manipulations Using Python 59 Chapter 5: Developing a Matrix Computation Class 79 Part 2: Understanding the Need to Sort and Search 97 Chapter 6: Structuring Data 99 Chapter 7: Arranging and Searching Data 117 Part 3: Exploring the World of Graphs 139 Chapter 8: Understanding Graph Basics 141 Chapter 9: Reconnecting the Dots 161 Chapter 10: Discovering Graph Secrets 195 Chapter 11: Getting the Right Web page 207 Part 4: Wrangling Big Data 223 Chapter 12: Managing Big Data 225 Chapter 13: Parallelizing Operations 249 Chapter 14: Compressing and Concealing Data 267 Part 5: Challenging Difficult Problems 289 Chapter 15: Working with Greedy Algorithms 291 Chapter 16: Relying on Dynamic Programming 307 Chapter 17: Using Randomized Algorithms 331 Chapter 18: Performing Local Search 349 Chapter 19: Employing Linear Programming 367 Chapter 20: Considering Heuristics 381 Part 6: The Part of Tens 401 Chapter 21: Ten Algorithms That Are Changing the World 403 Chapter 22: Ten Algorithmic Problems Yet to Solve 411 Index 417 ntroduction 1 Part 1: Getting Started with Algorithms 7 Chapter 1: Introducing Algorithms 9 Chapter 2: Considering Algorithm Design 23 Chapter 3: Working with Google Colab 41 Chapter 4: Performing Essential Data Manipulations Using Python 59 Chapter 5: Developing a Matrix Computation Class 79 Part 2: Understanding the Need to Sort and Search 97 Chapter 6: Structuring Data 99 Chapter 7: Arranging and Searching Data 117 Part 3: Exploring the World of Graphs 139 Chapter 8: Understanding Graph Basics 141 Chapter 9: Reconnecting the Dots 161 Chapter 10: Discovering Graph Secrets 195 Chapter 11: Getting the Right Web page 207 Part 4: Wrangling Big Data 223 Chapter 12: Managing Big Data 225 Chapter 13: Parallelizing Operations 249 Chapter 14: Compressing and Concealing Data 267 Part 5: Challenging Difficult Problems 289 Chapter 15: Working with Greedy Algorithms 291 Chapter 16: Relying on Dynamic Programming 307 Chapter 17: Using Randomized Algorithms 331 Chapter 18: Performing Local Search 349 Chapter 19: Employing Linear Programming 367 Chapter 20: Considering Heuristics 381 Part 6: The Part of Tens 401 Chapter 21: Ten Algorithms That Are Changing the World 403 Chapter 22: Ten Algorithmic Problems Yet to Solve 411 Index 417 ntroduction 1 Part 1: Getting Started with Algorithms 7 Chapter 1: Introducing Algorithms 9 Chapter 2: Considering Algorithm Design 23 Chapter 3: Working with Google Colab 41 Chapter 4: Performing Essential Data Manipulations Using Python 59 Chapter 5: Developing a Matrix Computation Class 79 Part 2: Understanding the Need to Sort and Search 97 Chapter 6: Structuring Data 99 Chapter 7: Arranging and Searching Data 117 Part 3: Exploring the World of Graphs 139 Chapter 8: Understanding Graph Basics 141 Chapter 9: Reconnecting the Dots 161 Chapter 10: Discovering Graph Secrets 195 Chapter 11: Getting the Right Web page 207 Part 4: Wrangling Big Data 223 Chapter 12: Managing Big Data 225 Chapter 13: Parallelizing Operations 249 Chapter 14: Compressing and Concealing Data 267 Part 5: Challenging Difficult Problems 289 Chapter 15: Working with Greedy Algorithms 291 Chapter 16: Relying on Dynamic Programming 307 Chapter 17: Using Randomized Algorithms 331 Chapter 18: Performing Local Search 349 Chapter 19: Employing Linear Programming 367 Chapter 20: Considering Heuristics 381 Part 6: The Part of Tens 401 Chapter 21: Ten Algorithms That Are Changing the World 403 Chapter 22: Ten Algorithmic Problems Yet to Solve 411 Index 417

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Book SynopsisMatching problems with preferences are all around us: they arise when agents seek to be allocated to one another on the basis of ranked preferences over potential outcomes. Efficient algorithms are needed for producing matchings that optimise the satisfaction of the agents according to their preference lists.In recent years there has been a sharp increase in the study of algorithmic aspects of matching problems with preferences, partly reflecting the growing number of applications of these problems worldwide. The importance of the research area was recognised in 2012 through the award of the Nobel Prize in Economic Sciences to Alvin Roth and Lloyd Shapley.This book describes the most important results in this area, providing a timely update to The Stable Marriage Problem: Structure and Algorithms (D Gusfield and R W Irving, MIT Press, 1989) in connection with stable matching problems, whilst also broadening the scope to include matching problems with preferences under a range of alternative optimality criteria.Table of ContentsPreliminary Definitions, Results and Motivation; Stable Matching Problems: The Stable Marriage Problem: An Update; SM and HR with Indifference; The Stable Roommates Problem; Further Stable Matching Problems; Other Optimal Matching Problems: Pareto Optimal Matchings; Popular Matchings; Profile-Based Optimal Matchings.

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Book SynopsisAn accessible introduction to algorithms, explaining not just what they are but how they work, with examples from a wide range of application areas.Digital technology runs on algorithms, sets of instructions that describe how to do something efficiently. Application areas range from search engines to tournament scheduling, DNA sequencing, and machine learning. Arguing that every educated person today needs to have some understanding of algorithms and what they do, in this volume in the MIT Press Essential Knowledge series, Panos Louridas offers an introduction to algorithms that is accessible to the nonspecialist reader. Louridas explains not just what algorithms are but also how they work, offering a wide range of examples and keeping mathematics to a minimum.After discussing what an algorithm does and how its effectiveness can be measured, Louridas covers three of the most fundamental applications areas: graphs, which describe networks, from eighteenth-century proble

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Book Synopsis"My absolute favorite for this kind of interview preparation is Steven Skiena’s The Algorithm Design Manual. More than any other book it helped me understand just how astonishingly commonplace … graph problems are -- they should be part of every working programmer’s toolkit. The book also covers basic data structures and sorting algorithms, which is a nice bonus. … every 1 – pager has a simple picture, making it easy to remember. This is a great way to learn how to identify hundreds of problem types." (Steve Yegge, Get that Job at Google)"Steven Skiena’s Algorithm Design Manual retains its title as the best and most comprehensive practical algorithm guide to help identify and solve problems. … Every programmer should read this book, and anyone working in the field should keep it close to hand. … This is the best investment … a programmer or aspiring programmer can make." (Harold Thimbleby, Times Higher Education)"It is wonderful to open to a random spot and discover an interesting algorithm. This is the only textbook I felt compelled to bring with me out of my student days.... The color really adds a lot of energy to the new edition of the book!" (Cory Bart, University of Delaware)"The is the most approachable book on algorithms I have." (Megan Squire, Elon University)---This newly expanded and updated third edition of the best-selling classic continues to take the "mystery" out of designing algorithms, and analyzing their efficiency. It serves as the primary textbook of choice for algorithm design courses and interview self-study, while maintaining its status as the premier practical reference guide to algorithms for programmers, researchers, and students. The reader-friendly Algorithm Design Manual provides straightforward access to combinatorial algorithms technology, stressing design over analysis. The first part, Practical Algorithm Design, provides accessible instruction on methods for designing and analyzing computer algorithms. The second part, the Hitchhiker's Guide to Algorithms, is intended for browsing and reference, and comprises the catalog of algorithmic resources, implementations, and an extensive bibliography. NEW to the third edition: -- New and expanded coverage of randomized algorithms, hashing, divide and conquer, approximation algorithms, and quantum computing -- Provides full online support for lecturers, including an improved website component with lecture slides and videos -- Full color illustrations and code instantly clarify difficult concepts -- Includes several new "war stories" relating experiences from real-world applications -- Over 100 new problems, including programming-challenge problems from LeetCode and Hackerrank. -- Provides up-to-date links leading to the best implementations available in C, C++, and Java Additional Learning Tools: -- Contains a unique catalog identifying the 75 algorithmic problems that arise most often in practice, leading the reader down the right path to solve them -- Exercises include "job interview problems" from major software companies -- Highlighted "take home lessons" emphasize essential concepts -- The "no theorem-proof" style provides a uniquely accessible and intuitive approach to a challenging subject -- Many algorithms are presented with actual code (written in C) -- Provides comprehensive references to both survey articles and the primary literature Written by a well-known algorithms researcher who received the IEEE Computer Science and Engineering Teaching Award, this substantially enhanced third edition of The Algorithm Design Manual is an essential learning tool for students and professionals needed a solid grounding in algorithms. Professor Skiena is also the author of the popular Springer texts, The Data Science Design Manual and Programming Challenges: The Programming Contest Training Manual.Table of ContentsIntroduction to Algorithm DesignAlgorithm AnalysisData StructuresSorting and SearchingDivide and ConquerRandomized Algorithms and HashingGraph TraversalWeighted Graph AlgorithmsCombinatorial Search and Heuristic MethodsDynamic ProgrammingNP-CompletenessDealing with Hard Problems How to Design Algorithms14 A Catalog of Algorithmic Problems 43715 Data Structures 43915.1 Dictionaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44015.2 Priority Queues . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44515.3 Sux Trees and Arrays . . . . . . . . . . . . . . . . . . . . . . . 44815.4 Graph Data Structures . . . . . . . . . . . . . . . . . . . . . . . . 45215.5 Set Data Structures . . . . . . . . . . . . . . . . . . . . . . . . . 45615.6 Kd-Trees . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46016 Numerical Problems 46516.1 Solving Linear Equations . . . . . . . . . . . . . . . . . . . . . . 46716.2 Bandwidth Reduction . . . . . . . . . . . . . . . . . . . . . . . . 47016.3 Matrix Multiplication . . . . . . . . . . . . . . . . . . . . . . . . 47216.4 Determinants and Permanents . . . . . . . . . . . . . . . . . . . 47516.5 Constrained/Unconstrained Optimization . . . . . . . . . . . . . 47816.6 Linear Programming . . . . . . . . . . . . . . . . . . . . . . . . . 48216.7 Random Number Generation . . . . . . . . . . . . . . . . . . . . 48616.8 Factoring and Primality Testing . . . . . . . . . . . . . . . . . . . 49016.9 Arbitrary-Precision Arithmetic . . . . . . . . . . . . . . . . . . . 49316.10Knapsack Problem . . . . . . . . . . . . . . . . . . . . . . . . . . 49716.11Discrete Fourier Transform . . . . . . . . . . . . . . . . . . . . . 50117 Combinatorial Problems 50517.1 Sorting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50617.2 Searching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51017.3 Median and Selection . . . . . . . . . . . . . . . . . . . . . . . . . 51417.4 Generating Permutations . . . . . . . . . . . . . . . . . . . . . . 51717.5 Generating Subsets . . . . . . . . . . . . . . . . . . . . . . . . . . 52117.6 Generating Partitions . . . . . . . . . . . . . . . . . . . . . . . . 52417.7 Generating Graphs . . . . . . . . . . . . . . . . . . . . . . . . . . 52817.8 Calendrical Calculations . . . . . . . . . . . . . . . . . . . . . . . 53217.9 Job Scheduling . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53417.10Satisability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53718 Graph Problems: Polynomial-Time 54118.1 Connected Components . . . . . . . . . . . . . . . . . . . . . . . 54218.2 Topological Sorting . . . . . . . . . . . . . . . . . . . . . . . . . . 54618.3 Minimum Spanning Tree . . . . . . . . . . . . . . . . . . . . . . . 54918.4 Shortest Path . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55418.5 Transitive Closure and Reduction . . . . . . . . . . . . . . . . . . 55918.6 Matching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56218.7 Eulerian Cycle/Chinese Postman . . . . . . . . . . . . . . . . . . 56518.8 Edge and Vertex Connectivity . . . . . . . . . . . . . . . . . . . . 56816 CONTENTS18.9 Network Flow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57118.10Drawing Graphs Nicely . . . . . . . . . . . . . . . . . . . . . . . 57418.11Drawing Trees . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57818.12Planarity Detection and Embedding . . . . . . . . . . . . . . . . 58119 Graph Problems: NP-Hard 58519.1 Clique . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58619.2 Independent Set . . . . . . . . . . . . . . . . . . . . . . . . . . . 58919.3 Vertex Cover . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59119.4 Traveling Salesman Problem . . . . . . . . . . . . . . . . . . . . . 59419.5 Hamiltonian Cycle . . . . . . . . . . . . . . . . . . . . . . . . . . 59819.6 Graph Partition . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60119.7 Vertex Coloring . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60419.8 Edge Coloring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60819.9 Graph Isomorphism . . . . . . . . . . . . . . . . . . . . . . . . . 61019.10Steiner Tree . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61419.11Feedback Edge/Vertex Set . . . . . . . . . . . . . . . . . . . . . . 61820 Computational Geometry 62120.1 Robust Geometric Primitives . . . . . . . . . . . . . . . . . . . . 62220.2 Convex Hull . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62620.3 Triangulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63020.4 Voronoi Diagrams . . . . . . . . . . . . . . . . . . . . . . . . . . 63420.5 Nearest Neighbor Search . . . . . . . . . . . . . . . . . . . . . . . 63720.6 Range Search . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64120.7 Point Location . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64420.8 Intersection Detection . . . . . . . . . . . . . . . . . . . . . . . . 64820.9 Bin Packing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65220.10Medial-Axis Transform . . . . . . . . . . . . . . . . . . . . . . . . 65520.11Polygon Partitioning . . . . . . . . . . . . . . . . . . . . . . . . . 65820.12Simplifying Polygons . . . . . . . . . . . . . . . . . . . . . . . . . 66120.13Shape Similarity . . . . . . . . . . . . . . . . . . . . . . . . . . . 66420.14Motion Planning . . . . . . . . . . . . . . . . . . . . . . . . . . . 66720.15Maintaining Line Arrangements . . . . . . . . . . . . . . . . . . . 67120.16Minkowski Sum . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67421 Set and String Problems 67721.1 Set Cover . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67821.2 Set Packing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68221.3 String Matching . . . . . . . . . . . . . . . . . . . . . . . . . . . 68521.4 Approximate String Matching . . . . . . . . . . . . . . . . . . . . 68821.5 Text Compression . . . . . . . . . . . . . . . . . . . . . . . . . . 69321.6 Cryptography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69721.7 Finite State Machine Minimization . . . . . . . . . . . . . . . . . 70221.8 Longest Common Substring/Subsequence . . . . . . . . . . . . . 70621.9 Shortest Common Superstring . . . . . . . . . . . . . . . . . . . . 709CONTENTS 1722 Algorithmic Resources 71322.1 Algorithm Libraries . . . . . . . . . . . . . . . . . . . . . . . . . 71322.1.1 LEDA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71322.1.2 CGAL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71422.1.3 Boost Graph Library . . . . . . . . . . . . . . . . . . . . . 71422.1.4 Netlib . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71422.1.5 Collected Algorithms of the ACM . . . . . . . . . . . . . 71522.1.6 GitHub and SourceForge . . . . . . . . . . . . . . . . . . . 71522.1.7 The Stanford GraphBase . . . . . . . . . . . . . . . . . . 71522.1.8 Combinatorica . . . . . . . . . . . . . . . . . . . . . . . . 71622.1.9 Programs from Books . . . . . . . . . . . . . . . . . . . . 71622.2 Data Sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71722.3 Online Bibliographic Resources . . . . . . . . . . . . . . . . . . . 71822.4 Professional Consulting Services . . . . . . . . . . . . . . . . . . 71823 Bibliography 719Index 771

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Book SynopsisAs one of the most comprehensive machine learning texts around, this book does justice to the field's incredible richness, but without losing sight of the unifying principles. Peter Flach's clear, example-based approach begins by discussing how a spam filter works, which gives an immediate introduction to machine learning in action, with a minimum of technical fuss. Flach provides case studies of increasing complexity and variety with well-chosen examples and illustrations throughout. He covers a wide range of logical, geometric and statistical models and state-of-the-art topics such as matrix factorisation and ROC analysis. Particular attention is paid to the central role played by features. The use of established terminology is balanced with the introduction of new and useful concepts, and summaries of relevant background material are provided with pointers for revision if necessary. These features ensure Machine Learning will set a new standard as an introductory textbook.Trade Review"This textbook is clearly written and well organized. Starting from the basics, the author skillfully guides the reader through his learning process by providing useful facts and insight into the behavior of several machine learning techniques, as well as the high-level pseudocode of many key algorithms." < /br>Fernando Berzal, Computing ReviewsTable of ContentsPrologue: a machine learning sampler; 1. The ingredients of machine learning; 2. Binary classification and related tasks; 3. Beyond binary classification; 4. Concept learning; 5. Tree models; 6. Rule models; 7. Linear models; 8. Distance-based models; 9. Probabilistic models; 10. Features; 11. In brief: model ensembles; 12. In brief: machine learning experiments; Epilogue: where to go from here; Important points to remember; Bibliography; Index.

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Book SynopsisAlmost half of all software projects fail to deliver on key requirements. Behavior-Driven Development (BDD) reduces these costly failures by building a shared understanding of how an application should work. Behavior Driven Development in Action, Second Edition teaches communication skills, collaborative practices, and automation tools that ensure everyone from developers to non-technical stakeholders are in agreement on the goals of a project. Revised and expanded in a second edition, the book contains new techniques for incorporating BDD into large-scale development practices such as Agile and DevOps, as well as updating examples for the latest versions of Java. about the technology You can't write good software if you don't understand what it's supposed to do. Behavior-Driven Development (BDD) encourages developers, quality teams, and non-technical stakeholders to collaborate, using conversation and concrete examples to make sure everyone agrees how an application should work and what features really matter. With a body of best practices and sophisticated tools for requirement analysis and test automation, BDD has become a mainstream practice for keeping projects on track and avoiding cancellation. what's inside BDD theory and practice How BDD will affect your team BDD for acceptance, integration, and unit testing Automating web services Reporting and living documentation about the reader For all development teams. No experience with BDD required. Examples written in Java.

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Book SynopsisMordechai (Moti) Ben-Ari is an Associate Professor in the Department of Science Teaching at the Weizmann Institute of Science in Rehovot, Israel.  He is the author of texts on Ada, concurrent programming, programming languages, and mathematical logic, as well as Just a Theory: Exploring the Nature of Science.  In 2004 he was honored with the ACM/SIGCSE Award for Outstanding Contribution to Computer Science Education.Table of ContentsContents Preface xi 1 What is Concurrent Programming? 1 1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Concurrency as abstract parallelism . . . . . . . . . . . . . . . . 2 1.3 Multitasking . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.4 The terminology of concurrency . . . . . . . . . . . . . . . . . 4 1.5 Multiple computers . . . . . . . . . . . . . . . . . . . . . . . . 5 1.6 The challenge of concurrent programming . . . . . . . . . . . . 5 2 The Concurrent Programming Abstraction 7 2.1 The role of abstraction . . . . . . . . . . . . . . . . . . . . . . . 7 2.2 Concurrent execution as interleaving of atomic statements . . . . 8 2.3 Justification of the abstraction . . . . . . . . . . . . . . . . . . . 13 2.4 Arbitrary interleaving . . . . . . . . . . . . . . . . . . . . . . . 17 2.5 Atomic statements . . . . . . . . . . . . . . . . . . . . . . . . . 19 2.6 Correctness . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 2.7 Fairness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 2.8 Machine-code instructions . . . . . . . . . . . . . . . . . . . . . 24 2.9 Volatile and non-atomic variables . . . . . . . . . . . . . . . . . 28 2.10 The BACI concurrency simulator . . . . . . . . . . . . . . . . . 29 2.11 Concurrency in Ada . . . . . . . . . . . . . . . . . . . . . . . . 31 2.12 Concurrency in Java . . . . . . . . . . . . . . . . . . . . . . . . 34 2.13 Writing concurrent programs in Promela . . . . . . . . . . . . . 36 2.14 Supplement: the state diagram for the frog puzzle . . . . . . . . 37 3 The Critical Section Problem 45 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 3.2 The definition of the problem . . . . . . . . . . . . . . . . . . . 45 3.3 First attempt . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 3.4 Proving correctness with state diagrams . . . . . . . . . . . . . . 49 3.5 Correctness of the first attempt . . . . . . . . . . . . . . . . . . 53 3.6 Second attempt . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 3.7 Third attempt . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 3.8 Fourth attempt . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 3.9 Dekker’s algorithm . . . . . . . . . . . . . . . . . . . . . . . . 60 3.10 Complex atomic statements . . . . . . . . . . . . . . . . . . . . 61 4 Verification of Concurrent Programs 67 4.1 Logical specification of correctness properties . . . . . . . . . . 68 4.2 Inductive proofs of invariants . . . . . . . . . . . . . . . . . . . 69 4.3 Basic concepts of temporal logic . . . . . . . . . . . . . . . . . 72 4.4 Advanced concepts of temporal logic . . . . . . . . . . . . . . . 75 4.5 A deductive proof of Dekker’s algorithm . . . . . . . . . . . . . 79 4.6 Model checking . . . . . . . . . . . . . . . . . . . . . . . . . . 83 4.7 Spin and the Promela modeling language . . . . . . . . . . . . . 83 4.8 Correctness specifications in Spin . . . . . . . . . . . . . . . . . 86 4.9 Choosing a verification technique . . . . . . . . . . . . . . . . . 88 5 Advanced Algorithms for the Critical Section Problem 93 5.1 The bakery algorithm . . . . . . . . . . . . . . . . . . . . . . . 93 5.2 The bakery algorithm for N processes . . . . . . . . . . . . . . 95 5.3 Less restrictive models of concurrency . . . . . . . . . . . . . . 96 5.4 Fast algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . 97 5.5 Implementations in Promela . . . . . . . . . . . . . . . . . . . . 104

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Book SynopsisWhat identity means in an algorithmic age: how it works, how our lives are controlled by it, and how we can resist itAlgorithms are everywhere, organizing the near limitless data that exists in our world. Derived from our every search, like, click, and purchase, algorithms determine the news we get, the ads we see, the information accessible to us and even who our friends are. These complex configurations not only form knowledge and social relationships in the digital and physical world, but also determine who we are and who we can be, both on and offline. Algorithms create and recreate us, using our data to assign and reassign our gender, race, sexuality, and citizenship status. They can recognize us as celebrities or mark us as terrorists. In this era of ubiquitous surveillance, contemporary data collection entails more than gathering information about us. Entities like Google, Facebook, and the NSA also decide what that information means, constructing our worlds and the identities wTrade ReviewWe Are Datais a gem!... This finely crafted book should help us to take a giant collective leap forward. * International Journal of Communication *We Are Dataspells out the implications of being made of data in the digital age: our new & algorithmic identity. John Cheney-Lippold shows how algorithmic logics that undergird the architecture, regulation, monetization, and uses of the Internet have changed the nature of human experience and identity. Through witty and accessible examples, he eloquently lays out the social and political consequences of transcoding lived identity into measurable types in our new world. Clearly written, carefully researched, timely and intelligent,We Are Datais a compelling and much-needed book. -- Alexandra Juhasz,Chair, Film Department, Brooklyn CollegeJohn Cheney-Lippolds deft examination of & measurable typesthe categories by which we are known and assessed, based on our datasheds light on contemporary societys encounter with information systems to scrutiny, and with those eager to identify us for their own ends.We Are Data goes beyond naming possible harms. It helps us think differently about what it means to be & seen by marketers, algorithms, or the NSA as members of shifting categoriesidentifications that structure us and our encounter with the world, but that we have little power to shape. -- Tarleton Gillespie,author of Wired Shut: Copyright and the Shape of Digital CultureThis book sparkles with brilliant insights. It offers us tools and a vocabulary through which we can think about the layers of identities that our data-conjured ghosts inhabit. I dont think I fully grasped the complexity of what these clouds of commercial data did with us and to us until I read We Are Data. -- Siva Vaidhyanathan,author of The Googlization of Everything—and Why We Should WorryWe Are Data is an inspiring and thought-provoking book to read, especially for those interested in the social, political, and cultural aspects of data. It draws on a wide range of well-known literature in the field of Internet and algorithm studies and further engages deeply with the philosophical aspects of the presented themes. * Mobile Media and Communication *If knowledge is indeed the means by which we can begin to challenge the digital status quo, then Cheney-Lippold has done much to forearm us by so capably elucidating the problem. * LSE Review of Books *The text moves beyond overdone topics of online privacy to look at how the lack of privacy of our data impacts identities It is the most appropriate for social science researchers and students. * Choice *We Are Data shows us just how powerful data can be and how that data affects who we are and who we can be. Cheney-Lippold addresses how data is (and always has been) a part of our lives through the discussionof categorization, control, subjectivity, and privacy. * Technical Communication *A heady and rewarding explanation of our lives in the data age. [Cheney-Lippold's] discussion of privacy...will fascinate many. Essential reading for anyone who cares about the internet's extraordinary impact on each of us and on our society. * Starred Kirkus Reviews *

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Book SynopsisThis book is a complete guide to using functions in Power Query and is designed to help users of all skill levels learn and master its various functions. The Ultimate Guide to Functions in Power Query begins with an introduction to Power Query and an overview of the different types of functions available, along with detailed explanations of how to use each of them. You'll see how to leverage power functions to process and transform large datasets from various sources and learn advanced techniques such as creating custom functions and using conditional statements. The book also covers best practices for using functions, including tips on how to optimize query performance and troubleshoot common errors. Using practical example applications, Author Omid Motamedisedeh demonstrates how to optimize your data processing workflows, saving time and boosting productivity. By the end of the book, readers will have a deep understanding of Power Query functions and be ableto apply their knowledTable of ContentsChapter 1: Introduction to Power Query.- Chapter 2: Data Types.- Chapter 3: Number Functions.- Chapter 4: Text Functions.- Chapter 5: Date and Time Functions.- Chapter 6: List Functions.- Chapter 7: Record Functions.- Chapter 8: Table Functions.- Chapter 9: Extracting from Data Sources.- Chapter 10: Other Functions.

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Book SynopsisTo lead a data science team, you need to expertly articulate technology roadmaps, support a data-driven culture, and plan a data strategy that drives a competitive business plan. In this practical guide, you'll learn leadership techniques the authors have developed building multiple high-performance data teams. In How to Lead in Data Science you'll master techniques for leading data science at every seniority level, from heading up a single project to overseeing a whole company's data strategy. You'll find advice on plotting your long-term career advancement, as well as quick wins you can put into practice right away. Throughout, carefully crafted assessments and interview scenarios encourage introspection, reveal personal blind spots, and show development areas to help advance your career. Leading a data science team takes more than the typical set of business management skills. You need specific know-how to articulate technology roadmaps, support a data-driven culture, and plan a data strategy that drives a competitive business plan. Whether you're looking to manage your team better or work towards a seat at your company's top leadership table, this book will show you how. Trade Review“Improveleadership skills, irrespective of the domain you are in.” Vishwesh RaviShrimali “Whether you are new to managing, new to data science, or just want tobe a better advocate for your data team there are a lot of tips to improve yourpractice.” MichaelPetrey “This is a book that surpasses the boundaries of mining data and coding,but warns you about not forgetting them in the effort to successfully lead datascience teams.” JesúsJuárez-Guerrero “Excellent book. Covers a large complex topic in a clear and understandableway.” GaryBake “Excellent and ambitious book that provides actionable insight on how tolead in data science. Filled with insightful vignettes, anecdotes, and casestudies to bring life and relevance to the frameworks and discussion.” MarcParadis

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Book SynopsisIs everything Information? This is a tantalizing question which emerges in modern physics, life sciences, astronomy and in today’s information and technology-driven society. In Powers of Two expert authors undertake a unique expedition - in words and images - throughout the world (and scales) of information. The story resembles, in a way, the classic Powers of Ten journeys through space: from us to the macro and the micro worlds . However, by following Powers of Two through the world of information, a completely different and timely paradigm unfolds. Every power of two, 1, 2, 4, 8…. tells us a different story: starting from the creation of the very first bit at the Big Bang and the evolution of life, through 50 years of computational science, and finally into deep space, describing the information in black holes and even in the entire universe and beyond…. All this to address one question: Is our universe made of information? In this book, we experience the Information Universe in nature and in our society and how information lies at the very foundation of our understanding of the Universe.From the Foreword by Robbert Dijkgraaf: This book is in many ways a vastly extended version of Shannon’s one-page blueprint. It carries us all the way to the total information content of the Universe. And it bears testimony of how widespread the use of data has become in all aspects of life. Information is the connective tissue of the modern sciences. […] Undoubtedly, future generations will look back at this time, so much enthralled by Big Data and quantum computers, as beholden to the information metaphor. But that is exactly the value of this book. With its crisp descriptions and evocative illustrations, it brings the reader into the here and now, at the very frontier of scientific research, including the excitement and promise of all the outstanding questions and future discoveries.Message for the e-reader of the book Powers of Two The book has been designed to be read in two-page spreads in full screen mode. For optimal reader experience in a downloaded .pdf file we strongly recommend you use the following settings in Adobe Acrobat Reader: - Taskbar: View > Page Display > two page view - Taskbar: View > Page Display > Show Cover Page in Two Page View - Taskbar: ^ Preferences > Full Screen > deselect " Fill screen with one page at a time" - Taskbar: View > Full screen mode or ctrl L (cmd L on a Mac) ***** Note: for reading the previews on Spinger link (and on-line reading in a browser), the full screen two-page view only works with these browsers: Firefox - Taskbar: on top of the text, at the uppermost right you will see then >> (which is a drop-down menu) >> even double pages - Fullscreen: F11 or Control+Cmd+F with Mac Edge - Taskbar middle: Two-page view and select show cover page separatelyTrade Review“The book … a very unusual collection of some facts about the relationship between the immaterial world represented by bits and the real physical world described by fundamental physical equations. This book continues the very categorical point of view of J. A. Wheeler … . The book presents short articles on various areas of modern science … in which it is shown that in these areas in some mysterious way there is a connection with the theory of information.” (Vladimir Dzhunushaliev, zbMATH 1479.83004, 2022)Table of ContentsForeword by Robbert DijkgraafChapter 0: IntroductionJoy-riding the Universe – by the authorWorking as an astronomer, data scientist and professor of astro-informatics for nearly fifty years, Edwin Valentijn has witnessed and first-hand engineered the dawn of the era of Big Data in science and society. Throughout his career, he became increasingly aware of the role of information in our world: in computers, in our society, and even in nature and in the Universe itself.The Information UniverseFollowing the increasing powers of two, the story paints a journey through the whole world of information, both in society and in nature. Each step opens a door into a new world: from the first bits with the Big Bang and the dawn of life, going through fifty years of human technology, all the way up to the information content of the whole Universe.What is Information? - Item pageThe basics of information are introduced.Chapter 1: The beginningSpace-time foam – Ti (0 bit: 20 =1)The very first power of two: 20, corresponds to the value one. This identifies the single, eternal, indistinguishable state: the primordial sea from which our Universe emerged – sometimes called the Space-time foam. I call this Ti, the reverse of It. This is one of the miraculous new notions in the story of the Powers of Two.Multiverse: Anthropic principle (Item page)From Ti, the primordial space-time foam, countless universes arise with widely different characteristics: the Multiverse. The Anthropic Principle is a philosophical consideration which states that we, people, will find ourselves in a universe that is suitable for intelligent life to emerge. Therefore, this Principle demonstrates that conditions in our Universe are not “fine-tuned” to the existence of human life and a “creator” doesn’t exist.Big bang (1 bit: 21 =2 states)At the Big Bang the first bit is created. From the indistinguishable unity of the primordial foam Ti, “the zeros were separated from the 1’s”: the first bit corresponds to two possible states. This bit is the first step on our journey to capture the ever-increasing complexity of our expanding Universe in terms of information, through the increasing powers of two.What is a bit? (Item page)The bit is at the core of the concept of information. A bit is any system that can have two states. Humans assign meanings to these states, which are illustrated with the concept of the traffic light: red or green, stop or go. The combination of multiple bits creates an exponentially increasing number of possible states, and hence meanings.Multicellular life (2 bit: 22 =4 states) / (4 bit: 24 =16 states)?Life started with exchanging information between cells. This is fundamental for the evolution of any kind of life. It took at least two billion years for uni-cellular to evolve into multi-cellular organisms around 600 million years ago, and to start the exchange of information between their different cells. By exchanging information, cells collaborate and act as a unified whole: life.The game of life (Item page)The characteristic features of life (or any complex system in the Universe) can be created from information. A simple computer game is all you need to demonstrate this concept. A famous example is Conway's Game of Life, which is full of visuals of living, growing, moving and dying objects. This game was already made on the computers of the early 70's with just a few lines of code.Chapter 2: People's Information UniverseASCII (7 bit: 27 =128 states)There is currently no physical theory how the digital world connects to the human consciousness. In the world of Information Technology (IT) all information exchange is based on agreements between people. For instance, ASCII, a simple list relating each letter of the alphabet to a 7-bit string, connects the digital world to the human consciousness. Machu Picchu (8 bit: 28 =256, 1 byte)The Intiwatana stone, a giant rock carved by the Inca's of ancient Machu Picchu in Peru, can be considered as a first 8-bit hard disk. Why so? As the sunrays lit the different surfaces of this huge rock throughout the year, it triggered the Inca's activities: sowing, harvesting, celebrating and praying.This ancient stone dissolves both the boundaries between heaven and earth, and those between the digital and natural Information Universe. In fact, the stone represents an ultimate picture of the cross-over between the in vivo and the in vitro Information Universe - a main theme of the book. In vitro being the man made technology to handle information and in vivo being the information built in nature, in this case the orbit and the light rays of the sun.First computers (16 bit: 216 =65.536, 2 byte)When computers emerged in the 1970's, astronomers first adopted them to steer their telescopes. Back then, a maximal effort to understand the mathematics of the problem was needed to squeeze the solution into the small computer memory. Nowadays, with large amounts of computing power and machine learning at their disposal, scientists and computer programmers often do the reverse.Star Peace vs. Star Wars (Item page)King Juan Carlos adored the harmony of galaxies as a source of inspiration for people on earth, in those days when Ronald Reagan was promoting his Star-wars programme. With this adoration in mind, in 1985, he gave an inspiring speech at the Royal inauguration of the international astronomical observatory on La Palma, Canary Islands. The inauguration was attended by, for those days, an unprecedented large crowd of European royals and government officials despite the great threat of terrorist attacks by the ETA. (the next and later spreads on facts vs fakes elucidate the relevance of this spread in the story line).Pre-internet Facts and Fakes (Item page)“Edwin Valentijn saved the life of the Dutch Queen Beatrix by catching her just before falling off a cliff at the inauguration on La Palma”, according to the headlines in Dutch newspapers. Fake news-stories are at all times alike and can only be dispelled by tracing links of information to their source, links or associations being a fundamental property of the Information Universe. Later, I discuss the less innocent case of overdrawing attention to terrorist attacks in the past decade.Hard disk (24 bit: 224 =1.6*107, 2 Mb)Only sixty years ago, a 5 MB hard disk weighed over five tons, and had to be loaded onto an aeroplane by using a truck. Now, we carry a thousand times more information in our trouser pocket. This demonstrates the amazing advance of information technology over the past decades. (Picture: first IBM hard disk loaded onto a plane).The telephone (Item page) As a precursor of the Internet, the telephone offered many of the same advantages and dangers, and was heavily discussed at its introduction. Whether telephone or the Internet, it all revolves around communication or copying of information. The telephone, as example of it, is one of the major discoveries of the 20th century. DNA (32 bit: 232 = 4*109, 500 Mb) – Guest author: Charley Lineweaver The information in the DNA creates life. All base pairs of the human DNA can be stored on a 500 Mb drive. How is this information communicated? How does a cell know it has to build part of a liver and not an eye, while they all have the same DNA? Apoptosis and the role of information exchange.Where does biological Information come from? (Item page) – Guest author: Charley Lineweaver Charley Lineweaver, expert on evolutionary biology, exoplanetology and astrobiology, will expand on the role of information in the evolution of life.Lifelines (Item page) – Guest author: Morris SwertzWhat is the role of nature versus that of nurture? A key question in modern health research. In Lifesciences, this question is addressed now using Big Data, like the astronomers who acquire huge data volumes to address the same question on the nature of galaxies. In Lifelines, a cohort of 165.000 people is studied over a period of 30 years using hospital data, blood samples and DNA scans.DVD (33 bit: 233 =9*109, 1 Gb)It’ s amazing how fast the digital image revolution went since 1989.30 years ago, Philips lab approached me since they had made a big discovery: it was possible to store many digital images on a CD. They were chasing me for digital images. While NASA had less than a thousand, I had 32.000 galaxy images obtained by scanning photographic plates from the European Southern Observatory – the first large digital image collection.Human Brain (36 bit: 236 =7*1010, 9 Gb) – Guest author: Katrin Amunts- JulichIn the large EU human brain project, the activities of the human brain are simulated in computers. This is a very difficult mission since the transistors in computers consume 100.000 billion times more energy than the synapsis of neurons. Our brains consist of 1011 neurons, corresponding to 9 Gb of data.Thinking of Karlheinz Meier, coordinator of the Human Brain Project in Heidelberg, Katrin Amunts will author two spreads on the role of information in the human brain.Neuromorphic computing – Guest author: Katrin AmuntsCurrently, it takes a hundred years of a supercomputer’s time to compete with the learning power of only a single day of the human brain. “Neuromorphic computing” researchers design electronic systems inspired by the human brain, in order to make computers many times faster and more energy efficient.CT scan (38 bit: 238 =3*1011, 34 Gb) – Guest author: Anders YnnermanNow it is possible to look inside animal and human bodies on touchscreens. Forensic investigations on, for instance, corpses of victims can be done with touch-screen tables. You can look inside, rotate, scroll and zoom animal and human bodies using tens of gigabytes of CT scan data. Prof. Anders Ynnerman explains how he does it.Terabytes (45 bit: 245 =4.4*1012, 1 Tb) - The largest (astronomical) datasetsDark energy and dark matter: two mysterious constituents of our Universe. How do astronomers get and handle the data from the VLT Survey Telescope on a high mountain top in Chile to shed lights on these ‘still too dark’ topics. This Telescope surveys the sky every hour at night generating Terabytes of astronomical data.Gravity as a lens (Item page) – Guest author: Margot BrouwerWhen light rays are bent by the gravity of a heavy object, this object acts as a lens. This effect can be used to map dark matter, which is invisible but constitutes 80% of the matter in our visible Universe. In 1915, Albert Einstein posed that gravity is equivalent to the curvature of the fabric of space and time itself, leading to the lensing effect.Weak gravitational lensing surveys – Guest author: Margot BrouwerTerabytes of astronomical data are reduced to a few numbers, describing how dark matter behaves and what is its true nature. https://www.youtube.com/watch?v=ZCyYGWqCmFw&t=23sEntering the Petabyte regime (53 bit: 253 =1*1015, 1 Pb)How do we technically acquire and deal with Petabytes of data?Dark Matter maps (Item page)A first dark matter map projected on the night sky. An ultimate encounter between the digital world of modern astronomical observations, and nature: the mysterious dark matter mapped on top of the everyday “night” stellar sky. A visualization that condenses Terabytes of astronomical data to a simple map.Metadata for Peta-data (62 bit: 262 =6*1017, 600 Pb)With pointers, one can connect everything in the Information Universe. Pointers are often inserted in Metadata (data about data) - an ultimate tool for dealing with Big Data. It is possible to create unique pointers to hundreds of Petabytes of data, using a string of less than 64 bits. This is what makes pointers so powerful and indispensable in current and future stages of the big data era; not only for astronomical research, but also for companies like Google, Amazon and Facebook.Downloading the Universe (Item page)The universe can be seen as a spreadsheet, certainly in the way we map it on our computers (in vitro), but also in nature (in vivo). Perceiving the Universe as a spreadsheet links bit to It.Meta data (Item page)A visualisation of the enormous complexity of data models which trace all pointers between data items. (picture: thrilling still from a full dome animation of a data model)Future (astronomical) datasets (item page)While current telescopes collect astronomical datasets of Terabytes, future telescopes such as the LSST and the Euclid satellite, instead, will collect Petabytes. These enormous amounts of data need a whole new approach to data management. For the Euclid satellite my “Universe as a spreadsheet” approach has been adopted.The Euclid satellite (Item page) – Guest author: Margot BrouwerEuclid is ESA’s new space mission to map the Dark Universe. At a distance of 1.5 million kilometres from Earth, this telescope will observe billions of galaxies. Its goal: to shed light on the nature of Dark Matter and Dark Energy, which make up 95% of our Universe. Dr. Margot Brouwer, Dutch scientific communication officer for Euclid, will explain more.The Information Universe (Item page)The resemblance of the overall structure of the real observed Universe (in vivo) with the simulated universe (in vitro), based on the concurrent cosmological model, gave a lot of credit to the latter. When we zoom out the Universe, we see billions of galaxies forming a web-like structure. Amazingly, astronomers can now compute and simulate these structures with very large supercomputers.The lost boy (Item page)Information is timeless, and knows no boundaries. It crosses over the in vivo and the in vitro Information Universe. This concept is well illustrated through daily life stories involving time. At the age of five, a boy loses sight of his older brother on a train in India, and eventually gets lost on the streets of Mumbai. Twenty years later, after being adopted by a family in Australia, he is able to find his natural mother (in vivo) through only searching on Google maps (in vitro).Qbits (50 qbit: 250 =1.1*1015 qbit, 1 Pbit) – Guest author: Lieven VandersypenUsing fundamental particles (quanta, such as electrons) to perform calculations and build computers, is one of the most exciting cross-overs between the in vivo and the in vitro Information Universe. Prof. Lieven Vandersypen, who leads a Quantum Computing group at TU Delft in the Netherlands, will explain how this technology will change the way we compute.Quantum entanglement (Item page) – Guest author: Lieven VandersypenThe states of two particles can be intimately linked (entangled), no matter how far they are separated. What Einstein famously dismissed as “spooky action at a distance”, can now be established on demand at TU Delft in the Netherlands. Prof. Vandersypen will explain how his research group, for the first time ever, both create and apply this entanglement in laboratory.Entanglement (item page) - EVThe Square Kilometre Array (64 bit: 264 =1.3*1018, 1 Eb) – Guest author: TBAThe Square Kilometre Telescope will collect data at the rate of the global internet traffic of 2013, in its endeavour to answer fundamental questions about the origin and evolution of the Universe, and its search for extra-terrestrial life.Cryptography (128 bit: 2128 =3.4*1038) – Guest author Tanja LangeEncrypted messages should not be decoded by adversaries, be they criminals or hostile countries. Cryptography enables secure communications and is one of the few applications which require 128-bit numbers. A guest author will explain more.Chapter 3: Deep spaceThe Desert (128-256 bit) Theoretical physics is not progressing much in the last decennia – some call it a crisis. Likely, an observational breakthrough is out of reach: the highest man-made information density on earth is produced by the high energy accelerators at CERN. But these accelerators have to be 1013 -1015 more powerful to reach the fundamental unit of information, which is probably at the same level of the Planck length. Unfortunately, there is no way to reach this unit of information with these instruments. This enormous gap in reaching all the domains in the Information Universe is illustrated in a figure and in a very sobering, but instructive table in the Appendix.Black holes (128-256 bit?) – Guest author: Manus VisserCan information disappear into a black hole? The Information paradox. Stephen Hawking wondered it and started a field in which space and time are described in terms of information. Dr. Manus Visser, expert on gravity and space-time, will explain more.Observing a Black Hole: Event Horizon Telescope – Guest author: Heino FalckeThe first image of a black hole. Prof. Heino Falcke, chair of the Event Horizon Telescope Science Council, will explain how information from a world-wide network of telescopes was combined using atomic clocks, to create the first ever image of a black hole. (Picture: first image of a black hole)Cogwheels: a deeper level – Guest author: Gerard 't HooftNobel laureate ‘t Hooft explains his views on cogwheels, carrying the fundamental information in the Universe.Gravitational waves – Guest author: Chris van den BroeckLinks: The Universe as a spreadsheetLinks, joins, references, URLs, blockchain, associations and even entanglement in physics are all different words for the same building block, forming the connections in the Information Universe.Cosmic Microwave Background – Guest author: Margot BrouwerParticles of light created in the hot and dense state of the Universe after the Big Bang are still flying through the Universe today. Together, these 1077 photons contain the largest amount of information known in the Universe. This information can still be accessed through telescopes, and brings us invaluable information about the dawn of our Universe.Emergent Gravity – Guest author: Erik VerlindeProf. Erik Verlinde, professor of theoretical physics at the University of Amsterdam, won the Spinoza prize for his new theory explaining gravity. In his theory, all matter, space and time consist of information and are all connected by entanglement. If this theory is correct, the information content of the entire Universe is 2399. This is the highest power described in this book, and actually, in physics.Chapter 4: It from BitOne big information processing machine – Guest author: Gerard 't Hooft (TBC)t Hooftt Hooft: : ““there is something happening at a different level of nature”there is something happening at a different level of nature”..On the origin of physical information. – Guest author: Stefano GottardiThe ear In the ear information is copied a dozen times!The eye – on the visual perception of data- climate change. Links to - facts and fakes- the system of ScienceThe System of ScienceHow does this system work? Discussing Hegel’s system of science, logic, technology, Nature, life, physics, consciousness.Artificial IntelligenceThe machine learning and the data-base oriented communities are still living on different planets. I discuss and revisit Tegmark’s recent book Life 3.0 by comparing 3 crosscuts through the Information Universe: i) the classical computer centric view ii) the data centric view iii) the artificial intelligence view.Information densityThe average information density of the universe can be compared to that of written text.Black Body radiation On the information aspects of the third big physical breakthrough of the 20th century (next to General relativity and quantum mechanics).EntropyDiscussing Shannon’s work and identifying that “Information only exists in relation to its environment”. Examples will be given.Cosmic information, cosmogenesis and dark energy by PadmanabhanCosmic information connects the cosmological constant to cosmogenesisIt from BitIs the Universe one big information processing machine?ConsciousnessVery little is known about the consciousness and I refrain from addressing the consciousness per se. A relevant list of about 5 facts we do know are listed. Any view on the relation between the consciousness and the Information Universe should at least deal with this list.Somnium – Musician Jacco Gardner performing at DOTLiveplanetarium at Eurosonic 2019 show case music festival- Inspired by Kepler’s Somnium – directed by EV The Information UniverseAn overview.Facts and fakesHow is all this related to the current facts and fakes issues on the Internet? How do you make sure that what you are reading is accurate and comes from a reliable source?The link between Open Science, FAIR and reliability of data.

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Book SynopsisIn this introductory textbook the author explains the key topics in cryptography. He takes a modern approach, where defining what is meant by "secure" is as important as creating something that achieves that goal, and security definitions are central to the discussion throughout.The author balances a largely non-rigorous style — many proofs are sketched only — with appropriate formality and depth. For example, he uses the terminology of groups and finite fields so that the reader can understand both the latest academic research and "real-world" documents such as application programming interface descriptions and cryptographic standards. The text employs colour to distinguish between public and private information, and all chapters include summaries and suggestions for further reading.This is a suitable textbook for advanced undergraduate and graduate students in computer science, mathematics and engineering, and for self-study by professionals in information security. While the appendix summarizes most of the basic algebra and notation required, it is assumed that the reader has a basic knowledge of discrete mathematics, probability, and elementary calculus.Trade Review“The goal of cryptography is to obfuscate data for unintended recipients. … The book is divided into four parts. … The book is very comprehensive, and very accessible for dedicated students.” (Klaus Galensa, Computing Reviews, computingreviews.com, October, 2016)“Cryptography made simple is a textbook that provides a broad coverage of topics that form an essential working knowledge for the contemporary cryptographer. It is particularly suited to introducing graduate and advanced undergraduate students in computer science to the concepts necessary for understanding academic cryptography and its impact on real-world practice, though it will also be useful for mathematicians or engineers wishing to gain a similar perspective on this material.” (Maura Beth Paterson, Mathematical Reviews, July, 2016)“This is a very thorough introduction to cryptography, aimed at lower-division undergraduates. It is an engineering textbook that uses modern mathematical terminology (such as groups and finite fields). … Bottom line: really for engineers, and a useful book if used carefully; the organization makes is easy to get overwhelmed by the background material before you get to the 'good stuff', and even the good stuff has an overwhelming amount of detail.” (Allen Stenger, MAA Reviews, maa.org, June, 2016)“This very thorough book by Smart (Univ. of Bristol, UK) is aimed at graduate students and advanced undergraduates in mathematics and computer science and intended to serve as a bridge to research papers in the field. … Summing Up: Recommended. Upper-division undergraduates through professionals/practitioners.” (C. Bauer, Choice, Vol. 53 (10), June, 2016)Table of ContentsModular Arithmetic, Groups, Finite Fields and Probability.- Elliptic Curves.- Historical Ciphers.- The Enigma Machine.- Information Theoretic Security.- Historical Stream Ciphers.- Modern Stream Ciphers.- Block Ciphers.- Symmetric Key Distribution.- Hash Functions and Message Authentication Codes.- Basic Public Key Encryption Algorithms.- Primality Testing and Factoring.- Discrete Logarithms.- Key Exchange and Signature Schemes.- Implementation Issues.- Obtaining Authentic Public Keys.- Attacks on Public Key Schemes.- Definitions of Security.- Complexity Theoretic Approaches.- Provable Security: With Random Oracles.- Hybrid Encryption.- Provable Security: Without Random Oracles.- Secret Sharing Schemes.- Commitments and Oblivious Transfer.- Zero-Knowledge Proofs.- Secure Multiparty Computation.

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Book SynopsisThis book is the result of several decades of teaching experience in data structures and algorithms. It is self-contained but does assume some prior knowledge of data structures, and a grasp of basic programming and mathematics tools. Basic Concepts in Algorithms focuses on more advanced paradigms and methods combining basic programming constructs as building blocks and their usefulness in the derivation of algorithms. Its coverage includes the algorithms' design process and an analysis of their performance. It is primarily intended as a textbook for the teaching of Algorithms for second year undergraduate students in study fields related to computers and programming.Klein reproduces his oral teaching style in writing, with one topic leading to another, related one. Most of the classical and some more advanced subjects in the theory of algorithms are covered, though not in a comprehensive manner. The topics include Divide and Conquer, Dynamic Programming, Graph algorithms, probabilistic algorithms, data compression, numerical algorithms and intractability. Each chapter comes with its own set of exercises, and solutions to most of them are appended.Related Link(s)Table of ContentsDivide and Conquer; Dynamic Programming; Minimum Spanning Tree; Shortest Paths; Primality; Compression; Pattern Matching; Fat Fourier Transform; Cryptography; NP Completeness; Approximations; Solutions to Selected Exercises;

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Book SynopsisA revealing look at how negative biases against women of color are embedded in search engine results and algorithms Run a Google search for black girlswhat will you find? Big Booty and other sexually explicit terms are likely to come up as top search terms. But, if you type in white girls, the results are radically different. The suggested porn sites and un-moderated discussions about why black women are so sassy or why black women are so angry presents a disturbing portrait of black womanhood in modern society. In Algorithms of Oppression, Safiya Umoja Noble challenges the idea that search engines like Google offer an equal playing field for all forms of ideas, identities, and activities. Data discrimination is a real social problem; Noble argues that the combination of private interests in promoting certain sites, along with the monopoly status of a relatively small number of Internet search engines, leads to a biased set of search algorithms that privilege whiteness and discriminaTrade ReviewRather than being a neutral arbiter that sorts content by quality, Noble argues that search engines are easily gamed in ways that reflect discriminatory practices. Even without malevolent actors, search engines may be perpetuating racist stereotypes. * Chicago Tribune *Nobles thesis is a new tune in the ever-louder chorus that, in light of the dominance of the big tech companies, is singing for 'protections and attention that work in service of the public'. * The Financial Times *[P]resents convincing evidence of the need for closer scrutiny and regulation of search engine[s].A thought-provoking, well-researched work. * Library Journal *Noble argues...that the web is ...a machine of oppression...[Her] central insight - that nothing about internet search and retrieval is political neutral - is made...through the accumulation of alarming and disturbing examples. [She] makes a compelling case that pervasive racism online inflames racist violence IRL. * Los Angeles Review of Books *A distressing account of algorithms run amok. * Kirkus Reviews *Algorithms of Oppressionis a wakeup call to bring awareness to the biases of the internet, and should motivate all concerned people to ask why those biases exist, and who they benefit. * New York Journal of Books *Noble offers a compelling look into the structure of digitized informationmost of it driven by advertising revenueand how it perpetuates racist assumptions and ideologies. * Pacific Standard *Noble makes a strong case that present technologies and search engines are not just imperfect, but they enact actual harm to people and communities. * Popmatters.com *50 Best Book of 2018 So Far, "There's been a growing swell of concern in the academic community about the stranglehold that commercial (for-profit) search engines have over access to information in our world. Safiya Umoja Noble builds on this body of work...to demonstrate that search engines, and in particular Google, are not simply imperfect machines, but systems designed by humans in ways that replicate the power structures of the western countries where they are built, complete with all the sexism and racism that are built into those structures. * Popmatters.com *Noble demolishes the popular assumption that Google is a values-free tool with no agenda...She astutely questions the wisdom of turning so much of our data and intellectual capital over to a corporate monopoly.Nobles study should prompt some soul-searching about our reliance on commercial search engines and about digital social equity. * STARRED Booklist *Nobles incisive work centers around the fact that, at present, Googles search engine promotes structural inequality through multiple examples and that this is not just a & design problem but an inherent political problem that has shaped the entirety of twentieth-century technology design. In addition to her illustrative examples and incisive criticism, Noble offers practicable policy solutions. * Metascience *In Algorithms of Oppression, [Noble] offers her readers a lens to discover, analyze, and critique the search engine algorithms that perpetuate stereotypes and racist beliefs[This] book will be of great interest to academic librarians who teach information literacy courses, as well as students and faculty in computer science, ethnic studies, gender studies, and mass communications. * Choice *A good read for anyone interested in how bias can be expressed by lines of code. Even those already familiar with the issues will find new insight in the connections and impact Noble outlines. The book is accessible even to those who are not well-versed in the technology of search engines. -- The International Journal of Information, Diversity, & Inclusion"Algorithms of Oppression succeeds as a critical intervention, one with a clear commitment to engaged scholarship that should lead to policy changes as well as changes in a field too white, American and male. For readers of this journal, the book is a powerful example of the vital contributions of Black Feminist Technology Studies... Noble demonstrates that engaged, intersectional and accessible writing can and indeed does make a difference." -- The International Journal of Press/PoliticsOften assumed by both developers and the general public to be value-neutral, the algorithmic structures through which human beings create, organize, and access content online are, Noble effectively argues, inescapably shaped by the logics of oppression that shape our interconnected lives … Algorithms provides a strong introduction, with concrete and replicable examples of algorithmic oppression, for those beginning to think critically about our internet-centric information ecosystem. For those already steeped in the rapidly growing literature of critical librarian and information studies, Algorithms will be a valuable addition to our corpus of texts that blend theory and practice, both documenting the problematic nature of where we are and the possibility of where we might arrive in future if we fight, collectively, to make it so. -- New England ArchivistsAlgorithms of Oppression offers a sobering portrait of the impact of our reliance on quick, freely accessible searches. Foregrounding her discussion in the context of the technological mechanisms and decision‐makers that drive results, Noble forces the reader to confront the rarely discussed risks and long‐term costs associated with easy‐to‐access, corporate‐sponsored information. -- Teachers College RecordAll search results are not created equal. Through deft analyses of software, society, and superiority, Noble exposes both the motivations and mathematics that make a & technologically redlined internet. Read this book to understand how supposedly race neutral zeros and ones simply dont add up. -- Matthew W. Hughey,Author of White Bound: Nationalists, Antiracists, and the Shared Meanings of RaceSafiya Noble has produced an outstanding book that raises clear alarms about the ways Google quietly shapes our lives, minds, and attitudes. Noble writes with urgency and clarity. This book is essential for anyone hoping to understand our current information ecosystem. -- Siva Vaidhyanathan,Author of The Googlization of Everything — and Why We Should WorrySafiya Nobles compelling and accessible book is an impressive survey of the impact of search and other algorithms on our understandings of racial and gender identity. Her study raises crucial questions regarding the power and control of algorithms, and is essential reading for understanding the way media works in the contemporary moment. -- Sarah Banet-Weiser,Author of Authentic™: The Politics of Ambivalence in a Brand CultureAlgorithms of Oppression shines a light not only on the way that new technologies both reaffirm hegemonies of the past and impose constraints on our futures, but also on how we ourselves are interpellated daily and voluntarily into these algorithmic processes. * This Year’s Work in Critical and Cultural Theory *Illustrates not only how the platforms and programmes we use in our daily life are created and built within a specific economic, racial, and gendered context, but that that context and those platforms enact and reinforce oppressive social relationships as we use them. * Archifacts *

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Book SynopsisAlgorithmic trading is now open to small organizations and individual traders using online platforms. The tool of choice for many traders today is Python and its ecosystem of powerful packages. In this practical book, author Yves Hilpisch shows students, academics, and practitioners how to use Python in the fascinating field of algorithmic trading.

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Book SynopsisIf you thought that data structures and algorithms were all just theory, you're missing out on what they can do for your code. Learn to use Big O Notation to make your code run faster by orders of magnitude. Choose from data structures such as hash tables, trees, and graphs to increase your code's efficiency exponentially. With simple language and clear diagrams, this book makes this complex topic accessible, no matter your background. This new edition features practice exercises in every chapter, and new chapters on topics such as dynamic programming and heaps and tries. Get the hands-on info you need to master data structures and algorithms for your day-to-day work. Algorithms and data structures are much more than abstract concepts. Mastering them enables you to write code that runs faster and more efficiently, which is particularly important for today's web and mobile apps. Take a practical approach to data structures and algorithms, with techniques and real-world scenarios that you can use in your daily production code, with examples in JavaScript, Python, and Ruby. This new and revised second edition features new chapters on recursion, dynamic programming, and using Big O in your daily work. Use Big O notation to measure and articulate the efficiency of your code, and modify your algorithm to make it faster. Find out how your choice of arrays, linked lists, and hash tables can dramatically affect the code you write. Use recursion to solve tricky problems and create algorithms that run exponentially faster than the alternatives. Dig into advanced data structures such as binary trees and graphs to help scale specialized applications such as social networks and mapping software. You'll even encounter a single keyword that can give your code a turbo boost. Practice your new skills with exercises in every chapter, along with detailed solutions. Use these techniques today to make your code faster and more scalable

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Book SynopsisThe authors' treatment of data structures in Data Structures and Algorithms is unified by an informal notion of "abstract data types," allowing readers to compare different implementations of the same concept. Algorithm design techniques are also stressed and basic algorithm analysis is covered. Most of the programs are written in Pascal.

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Book SynopsisIn this practical book, author George Heineman (Algorithms in a Nutshell) provides concise and informative descriptions of key algorithms that improve coding. Software developers, testers, and maintainers will discover how algorithms solve computational problems creatively.

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Book SynopsisAlgorithmic puzzles are puzzles involving well-defined procedures for solving problems. This book will provide an enjoyable and accessible introduction to algorithmic puzzles that will develop the reader''s algorithmic thinking.The first part of this book is a tutorial on algorithm design strategies and analysis techniques. Algorithm design strategies -- exhaustive search, backtracking, divide-and-conquer and a few others -- are general approaches to designing step-by-step instructions for solving problems. Analysis techniques are methods for investigating such procedures to answer questions about the ultimate result of the procedure or how many steps are executed before the procedure stops. The discussion is an elementary level, with puzzle examples, and requires neither programming nor mathematics beyond a secondary school level. Thus, the tutorial provides a gentle and entertaining introduction to main ideas in high-level algorithmic problem solving.The second and main part of the bTrade ReviewIndeed, I would say that this is a book that any mathematical puzzle enthusiast ought to consider buying. * Martin Griffiths, The Mathematical Gazette *Table of ContentsPreface ; List of Puzzles ; Tutorial Puzzles ; Main Section Puzzles ; 1. Tutorials ; General Strategies for Algorithm Design ; Analysis Techniques ; 2. Puzzles ; Easier Puzzles (#1 - #50) ; Medium Dic culty Puzzles (51 - 110) ; Harder Puzzles (#111 - 150) ; 3. Hints ; 4. Solutions ; References ; Design Strategy and Analysis Index ; Index of Terms and Names

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Book SynopsisFor anyone who has ever wondered how computers solve problems, an engagingly written guide for nonexperts to the basics of computer algorithms.Have you ever wondered how your GPS can find the fastest way to your destination, selecting one route from seemingly countless possibilities in mere seconds? How your credit card account number is protected when you make a purchase over the Internet? The answer is algorithms. And how do these mathematical formulations translate themselves into your GPS, your laptop, or your smart phone? This book offers an engagingly written guide to the basics of computer algorithms. In Algorithms Unlocked, Thomas Cormen—coauthor of the leading college textbook on the subject—provides a general explanation, with limited mathematics, of how algorithms enable computers to solve problems.Readers will learn what computer algorithms are, how to describe them, and how to evaluate them. They will discover simple ways to search for information in a computer; methods for rearranging information in a computer into a prescribed order (“sorting”); how to solve basic problems that can be modeled in a computer with a mathematical structure called a “graph” (useful for modeling road networks, dependencies among tasks, and financial relationships); how to solve problems that ask questions about strings of characters such as DNA structures; the basic principles behind cryptography; fundamentals of data compression; and even that there are some problems that no one has figured out how to solve on a computer in a reasonable amount of time.

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Book SynopsisTurn your love of video games into a new love of programming by learning the ins and outs of writing code while also learning how to keep track of high scores, what video game heroes and loot boxes are made of, how the dreaded RNG (random number generation) works, and much, much more. This book is the first in an ongoing series designed to take readers from no coding knowledge to writing their own video games and interactive digital experiences using industry standard languages and tools. But coding books are technical, boring, and scary, arenât they? Not this one. Within these pages, readers will find a fun and approachable adventure that will introduce them to the essential programming fundamentals like variables, computer-based math operations, RNG, logic structures, including if-statements and loops, and even some object-oriented programming. Using Visual Studio and C#, readers will write simple but fun console programs and text-based games that will build coding skills a

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Book SynopsisProbabilistic numerical computation formalises the connection between machine learning and applied mathematics. Numerical algorithms approximate intractable quantities from computable ones. They estimate integrals from evaluations of the integrand, or the path of a dynamical system described by differential equations from evaluations of the vector field. In other words, they infer a latent quantity from data. This book shows that it is thus formally possible to think of computational routines as learning machines, and to use the notion of Bayesian inference to build more flexible, efficient, or customised algorithms for computation. The text caters for Masters'' and PhD students, as well as postgraduate researchers in artificial intelligence, computer science, statistics, and applied mathematics. Extensive background material is provided along with a wealth of figures, worked examples, and exercises (with solutions) to develop intuition.Trade Review'Computational methods for solving numerical problems lie at the heart of many of the technological advances in science and engineering over the last five decades, and underpin fields as diverse as artificial intelligence, climate modelling, and epidemiology. This impressive text rethinks numerical problems through the lens of probabilistic inference and decision making. This fresh perspective opens up a new chapter in this field, and suggests new and highly efficient methods. A landmark achievement!' Zoubin Ghahramani, University of Cambridge'This beautiful book is both timely and important with deep roots in powerful early exposition in numerical analysis. In this stunning and comprehensive new book, early developments from Kac and Larkin have been comprehensively built upon, formalised and extended by including modern day machine learning, numerical analysis and the formal Bayesian statistical methodology. Probabilistic Numerical methodology is of enormous importance for this age of data-centric science and Hennig, Osborne and Kersting are to be congratulated in providing us with this definitive volume.' Mark Girolami, University of Cambridge and The Alan Turing Institute'Numerical analysis is at the very heart of digital computing: every result of a computation on a digital computer is a only finite-precision representation of the true mathematical quantity where the precision is the tradeoff between computation time and accuracy. This book presents an in-depth overview of both the past and present of the newly emerging area of probabilistic numerics, where recent advances in probabilistic machine learning are used to develop principled improvements which are both faster and more accurate than classical numerical analysis algorithms. A must-read for every algorithm developer and practitioner in optimization!' Ralf Herbrich, Hasso Plattner Institute'Probabilistic Numerics spans from the intellectual fireworks of the dawn of a new field to its practical algorithmic consequences. It is precise but accessible and rich in wide-ranging, principled examples. This convergence of ideas from diverse fields in lucid style is the very fabric of good science.' Carl Edward Rasmussen, University of Cambridge'An important read for anyone who has thought about uncertainty in numerical methods; an essential read for anyone who hasn't …' John Cunningham, Columbia University'This is a rare example of a textbook that essentially founds a new field, re-casting numerics on stronger, more general foundations. A tour de force.' David Duvenaud, University of Toronto'The idea of applying probabilistic inference to the problem of numerical analysis must appear bold, possibly outrageous, even to an entrenched Bayesian statistician. Many in machine learning are now familiar with the application of Bayesian methods to problems that involve randomness, say, the estimation of quantities from noisy data. But to apply the 'calculus of uncertainty' to unknown mathematical facts, where the uncertainty arises only from our lack of knowledge, opens up a universe of new possibilities. This elegant idea is at the core of Probabilistic Numerics, and the authors succeed in demonstrating its potential to transform the way we think about computation itself. And that's not even considering what would happen if we were to apply probabilistic numerics to the numerical problems that arise from probabilistic numerics itself!' Thore Graepel, Senior Vice President, Altos Labs'… the machine learning background of the authors comes through clearly in the book … I thoroughly recommend it.' Chris J. Oates, SIAM ReviewTable of ContentsIntroduction; 1. Mathematical background; 2. Integration; 3. Linear algebra; 4. Local optimisation; 5. Global optimisation; 6. Solving ordinary differential equations; 7. The frontier; Solutions to exercises; References; Index.

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Book SynopsisGame theory is the science of interaction. This textbook, derived from courses taught by the author and developed over several years, is a comprehensive, straightforward introduction to the mathematics of non-cooperative games. It teaches what every game theorist should know: the important ideas and results on strategies, game trees, utility theory, imperfect information, and Nash equilibrium. The proofs of these results, in particular existence of an equilibrium via fixed points, and an elegant direct proof of the minimax theorem for zero-sum games, are presented in a self-contained, accessible way. This is complemented by chapters on combinatorial games like Go; and, it has introductions to algorithmic game theory, traffic games, and the geometry of two-player games. This detailed and lively text requires minimal mathematical background and includes many examples, exercises, and pictures. It is suitable for self-study or introductory courses in mathematics, computer science, or econoTrade Review'This looks like a fine introduction to game theory, inter alia emphasizing methods for computing equilibria, and mathematical aspects in general. Especially worthy of note is the chapter devoted to correlated equilibria, a topic of central importance not normally covered in introductory texts.' Robert Aumann, The Hebrew University of Jerusalem'This book is a delightful adventure into the mathematics of game theory. Without any heavy apparatus, it lets us into the secrets of a whole range of exciting results that are usually thought too advanced for the common herd. It is not only undergraduate students who will benefit from reading this book. Professional game theorists will find it very useful too.' Ken Binmore, University College London'Bernhard von Stengel's book will enable students to become intimately familiar with game theoretic reasoning, which is mathematical by nature. The text comes at the right time: Game theory has become so popular in economics and political science that teachers could be tempted to put the cart before the horse. Here, the basic noncooperative game models are studied gradually and thoroughly, in a unified way, while providing the algorithms that can be used to solve interactive decision problems.' Françoise Forges, Université Paris-Dauphine'This is a rather reader-friendly, engaging, and polished superior creation. It illustrates, explains, motivates every definition, theorem, proof. Interesting and unique choice of topics, such as a delightful introductory chapter on combinatorial games. Highly recommended.' Aviezri Fraenkel, Weizmann Institute of Science, Israel'A masterful presentation of mathematical game theory in all its beauty and elegance, from basic notions to advanced techniques. It fills the gaps left by the many textbooks that cover concepts and applications, but devote only the bare minimum to the mathematical tools and insights, without which game theory would not have become the success it is today.' Sergiu Hart, The Hebrew University of Jerusalem'Game Theory is the child of mathematicians, as this textbook demonstrates through self-contained, elegant proofs of all seminal Theorems. The lively and rigorous exposition of carefully selected models, such as bargaining, combinatorial and congestion games (the latter two rarely the stuff of textbooks) explains its success far beyond mathematics. To reach deep results on both sides of the theory, Bernhard von Stengel's marvellous learning tool uses uncompromising, yet accessible mathematics and chooses examples to maximal effect.' Hervé Moulin, University of Glasgow'This will become a classic textbook on non-cooperative game theory. It is very useful for mathematicians, computer scientists, and economic theorists. Each chapter has a clear learning structure, with motivating examples and a central main theorem. The author's long teaching experience and expertise in game theory is apparent on every page.' Abraham Neyman, The Hebrew University of Jerusalem'Attractively covers of a lot of important material, in particular for students of mathematics and computer science.' Eva Tardos, Cornell University'This book is a gem. The presentation is clear and well structured, often with nice geometric illustrations. It moves step by step from basics to powerful concepts, methods and results. It is ideal for students of mathematics, computer science and economics who are curious about what game theory is and how it can be used.' Jörgen Weibull, Stockholm School of Economics'This excellent text develops with clarity and precision the basic concepts and mathematical tools of game theory, enhanced by well-motivated examples, exercises, and practical applications.' Robert Wilson, Stanford University'An exceptionally lucid introduction to the fundamentals of game theory, enlivened by examples that are sure to captivate students.' Peyton Young, University of Oxford'This is a rigorous, yet accessible introduction to mathematical non-cooperative game theory. In addition to the coverage of the basic concepts and results, it includes special and advanced topics and applications usually not contained in game theory textbooks, such as combinatorial games, congestion games and inspection games. The special emphasis on algorithmic and computational techniques make this textbook, just like its author, a valuable bridge between game theory and computer sciences.' Shmuel Zamir, The Hebrew University of JerusalemTable of Contents1. Nim and Combinatorial Games; 2. Congestion Games; 3. Games in Strategic Form; 4. Game Trees with Perfect Information; 5. Expected Utility; 6. Mixed Equilibrium; 7. Brouwer's Fixed-Point Theorem; 8. Zero-Sum Games; 9. Geometry of Equilibria in Bimatrix Games; 10. Game Trees with Imperfect Information; 11. Bargaining; 12. Correlated Equilibrium.

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Book SynopsisTable of ContentsIntroduction xvii Chapter 1 Handling Date, Time, Text, Numeric and Boolean Values 1 Chapter 2 Controlling Program Flow 25 Chapter 3 Utilizing Java Object- Oriented Approach 45 Chapter 4 Handling Exceptions 149 Chapter 5 Working with Arrays and Collections 181 Chapter 6 Working with Streams and Lambda Expressions 211 Chapter 7 Packaging and Deploying Java Code and Use the Java Platform Module System 267 Chapter 8 Managing Concurrent Code Execution 295 Chapter 9 Using Java I/O API 319 Chapter 10 Accessing Databases Using JDBC 339 Chapter 11 Implementing Localization 353 Chapter 12 Practice Exam 1 365 Chapter 13 Practice Exam 2 391 Chapter 14 Practice Exam 3 417 Appendix Answers to Review Questions 443 Chapter 1: Handling Date, Time, Text, Numeric and Boolean Values 444 Chapter 2: Controlling Program Flow 450 Chapter 3: Utilizing Java Object- Oriented Approach 455 Chapter 4: Handling Exceptions 482 Chapter 5: Working with Arrays and Collections 489 Chapter 6: Working with Streams and Lambda Expressions 498 Chapter 7: Packaging and Deploying Java Code and Use the Java Platform Module System 516 Chapter 8: Managing Concurrent Code Execution 524 Chapter 9: Using Java I/O API 530 Chapter 10: Accessing Databases Using JDBC 535 Chapter 11: Implementing Localization 538 Chapter 12: Practice Exam 1 541 Chapter 13: Practice Exam 2 548 Chapter 14: Practice Exam 3 554 Index 561

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Book SynopsisA high school student can create deep Q-learning code to control her robot, without any understanding of the meaning of ''deep'' or ''Q'', or why the code sometimes fails. This book is designed to explain the science behind reinforcement learning and optimal control in a way that is accessible to students with a background in calculus and matrix algebra. A unique focus is algorithm design to obtain the fastest possible speed of convergence for learning algorithms, along with insight into why reinforcement learning sometimes fails. Advanced stochastic process theory is avoided at the start by substituting random exploration with more intuitive deterministic probing for learning. Once these ideas are understood, it is not difficult to master techniques rooted in stochastic control. These topics are covered in the second part of the book, starting with Markov chain theory and ending with a fresh look at actor-critic methods for reinforcement learning.Trade Review'Control Systems and Reinforcement Learning is a densely packed book with a vivid, conversational style. It speaks both to computer scientists interested in learning about the tools and techniques of control engineers and to control engineers who want to learn about the unique challenges posed by reinforcement learning and how to address these challenges. The author, a world-class researcher in control and probability theory, is not afraid of strong and perhaps controversial opinions, making the book entertaining and attractive for open-minded readers. Everyone interested in the "why" and "how" of RL will use this gem of a book for many years to come.' Csaba Szepesvári, Canada CIFAR AI Chair, University of Alberta, and Head of the Foundations Team at DeepMind'This book is a wild ride, from the elements of control through to bleeding-edge topics in reinforcement learning. Aimed at graduate students and very good undergraduates who are willing to invest some effort, the book is a lively read and an important contribution.' Shane G. Henderson, Charles W. Lake, Jr. Chair in Productivity, Cornell University'Reinforcement learning, now the de facto workhorse powering most AI-based algorithms, has deep connections with optimal control and dynamic programing. Meyn explores these connections in a marvelous manner and uses them to develop fast, reliable iterative algorithms for solving RL problems. This excellent, timely book from a leading expert on stochastic optimal control and approximation theory is a must-read for all practitioners in this active research area.' Panagiotis Tsiotras, David and Andrew Lewis Chair and Professor, Guggenheim School of Aerospace Engineering, Georgia Institute of TechnologyTable of Contents1. Introduction; Part I. Fundamentals Without Noise: 2. Control crash course; 3. Optimal control; 4. ODE methods for algorithm design; 5. Value function approximations; Part II. Reinforcement Learning and Stochastic Control: 6. Markov chains; 7. Stochastic control; 8. Stochastic approximation; 9. Temporal difference methods; 10. Setting the stage, return of the actors; A. Mathematical background; B. Markov decision processes; C. Partial observations and belief states; References; Glossary of Symbols and Acronyms; Index.

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Book SynopsisTable of ContentsPreface xiii Introduction xix Part I The Fun Part Chapter 1 Is Data Analytics Right for Me? 3 What Does a Data Analyst Do Every Day? 4 Hours/Time 6 In-Person Data Jobs 9 What Makes a Good Analyst? 10 Planning 12 Organization 13 Critical Thinking/Strategy 14 Collaboration/Communication 15 What Tools Should I Learn? 17 Excel/Google Sheets 17 SQL 19 Tableau/Power BI 21 Python 24 R 25 Which Entry-Level Tech Job Is Right for Me? 25 What’s Next 29 Chapter 2 Understanding the Paths into Data 31 How Hard Is It to Become a Data Analyst? 32 What Are My Options for Getting into Data Analytics? 34 Transitioning from an Analyst-Adjacent Role 35 Getting a Degree 35 Boot Camps 36 When a Boot Camp May Be the Right Option for You 37 How to Pick a Good Boot Camp 38 DIY Approach 40 How I Decided on the DIY Approach 41 Chapter 3 Designing Your Data Analyst Roadmap 45 Can You Shows Me Your Data Analyst Roadmap? 46 Building Your Roadmap 46 Step 1: Skill Development 47 Step 2: Building a Portfolio 49 Step 3: Getting Yourself Ready to Job Search 52 How Do I Choose the Best Course? 53 What Makes a Good Course 55 Learning Styles 55 Budget 56 Support 57 Interests 58 Time Constraints 59 Getting Started for Free 60 When Not to Pick a Course: How to Avoid Course Hopping 61 Chapter 4 My Experience with Data Analytics Courses 63 The Beginning 63 The Google Certificates Course 64 Learning SQL 65 Learning Tableau and R 68 Finishing the Course 70 What Came Next 72 Changing Careers 72 Course Hopping: When Is Taking Another Course Worth It? 73 Part II The Scary Part 77 Chapter 5 Introduction to Portfolios 79 What Is a Data Analytics Portfolio? 79 Can I See an Example? 80 Why Do I Need a Portfolio? 81 As an Analyst 81 As a Job Seeker 82 If I Have Experience from Another Job, Do I Still Need a Portfolio? 83 Chapter 6 Portfolio Project FAQ 85 How Do I Find Free Data? 86 Maven Analytics 87 Real World Fake Data 89 Your Data 89 Data from Me! 90 SQL Practice 91 Other Places 92 Can You Tell Me More about Completing Projects? 93 How Do I Get Started on Projects? 93 Does My Project Need to Be Original and Industry Specific? 95 How Do I Know When a Project Is Ready? 96 Where Do I Publish and Store My Work? 96 How Many Projects Do I Need? 98 Should I Share My Work Publicly? 99 Project Time! 100 Chapter 7 Portfolio Project Handbook 101 Project Levels: What Separates a Beginner from an Intermediate Project? 102 First Project 102 Beginner Project 103 Intermediate Project 103 Regular Tableau User 104 Guided Projects 104 New Year’s Eve Resolutions Project 104 Case Study: New Year’s Eve Resolutions Project 105 Semi-Structured Case Study with Hints 106 Final Thoughts 108 Help Desk Project 108 Case Study: Help Desk Project 109 Semi-Structured Prompts 109 Pizza Sales Project 111 Case Study: Pizza Sales Project 111 Dirty Data + Case Study 112 Dirty Data + Case Study + Hints 113 Clean Data + Case Study 114 Semi-Structured Case Study with Hints 115 Busy Times 116 Pizzas During Peak Periods 116 Best- and Worst-Selling Pizzas 116 Average Order Value 117 Seating Capacity 117 Final Thoughts 118 SQL Project Creation Advice 119 From the Portfolio to the Job Search 121 Getting in the Mindset for Projects 122 Part III The Hard Part 125 Chapter 8 Starting Your Job Search 127 How Do I Know When I Am Ready to Start My Job Search? 127 Where and How Should I Look for Jobs? 129 Searching Posts 129 Job Titles 130 Where Can I Find Salary Information? 131 What Is the Data Analyst Career Progression? 131 Chapter 9 Résumé Building and Setting Your Public Image 137 How Do I Write a Résumé? 138 Length 138 Technical Skills 141 Relevant History 142 Formatting 142 Use Metrics 143 How Do I Optimize My LinkedIn? 144 History 144 Connections 146 Headline 149 Profile Photo 150 Can You Tell Me How to Network? 151 What Is Networking (and What Is It Not)? 151 Networking and Messaging on LinkedIn 153 Messaging Jobs Directly 154 Networking Events 156 Interviewing 157 Bonus Tip: An Idea for Your First LinkedIn Post 158 Chapter 10 Stages of Data Interviews 161 Why Do Interviews Take So Long? 161 Can You Tell Me More about the Interview Stages? 162 Phone Screen 163 Meeting the Hiring Manager 165 Behavioral Interview 166 Technical Interview 166 Panel Interview 169 Culture Fit 170 Follow-up 171 How I Handled Some Common How-Tos 172 Tell Me about Yourself 173 How to Come Up with Good Questions 175 Resources 180 Teal 180 Maven Analytics 181 Content Creators/Small Businesses 182 Working with Data Creators 183 Using AI 184 Chapter 11 How to Use ChatGPT to Aid Your Job Search 185 Writing a Résumé 185 Writing Cover Letters 186 Practicing for Interviews 186 Phone Screen 186 Technical Interview 189 Behavioral Interview 189 Writing Follow-Up Emails 191 Be Specific 192 Chapter 12 My Job Search 195 “Open to Work?” 195 Beginning to Search 197 Getting Reponses (and Rejections) 200 Pivoting 202 Interviewing 204 Decision Day 207 Part IV The Bonus Part 209 Chapter 13 After the Job Offer 211 Starting the Job 212 Dealing with Imposter Syndrome 213 Steps to Success 214 What It’s Like Working Remotely 215 Some Things About Tech That Surprised Me 217 121s 217 Home Office Stipend 217 Company Party/Offsites 218 Meetings 218 Referrals 219 Layoffs 220 Problem‐ Solving 221 Travel 222 Data Has Changed My Life 224 Chapter 14 Preparing for/Recovering from a Layoff 225 Don’t Ignore Red Flags 225 Resumes and Networking—Restarting the Job Search 226 Updating my Portfolio 229 The Layoff 230 Adjusting for Your Situation 235 Closing Thoughts 236 Appendix A Data Analytics Roadmap Checklist 239 Appendix B Tableau Tips 241 Appendix C My Data Analyst Journey 249 Acknowledgments 257 About the Author 259 Index 261

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Book SynopsisThis book describes the creation of an actual generic open source big data stack, which is an integrated stack of big data components--each of which serves a specific function like storage, resource management, or queueing. Each component has a big data heritage and community to support it. It can support big data in that it is able to scale, and it is a distributed and robust system.In the Complete Guide to Open Source Big Data Stack, Mike Frampton begins by creating a private cloud and then by installing and examining Apache Brooklyn. After that he will use each chapter to introduce one piece of the big data stacksharing how to source the software and then how to install it. He will then show how it works by simple example. Step by step and chapter by chapter, Frampton will create a real big data stack. The goal of this book is to show how a big data stack might be created and what components might be used. It attempts to do this with currently available ApaTable of ContentsChapter 1: The Big Data Stack Overview.- Chapter 2: Cloud Storage.- Chapter 3: Apache Brooklyn.- Chapter 4: Apache Mesos.- Chapter 5: Stack Storage Options.- Chapter 6: Processing.- Chapter 7: Streaming.- Chapter 8: Frameworks.- Chapter 9: Visualization.- Chapter 10: The Big Data Stack.-

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Book SynopsisThis updated edition of Algorithms in a Nutshell describes a large number of existing algorithms for solving a variety of problems, and helps you select and implement the right algorithm for your needs-with just enough math to let you understand and analyze algorithm performance.

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Book SynopsisUpdated to emphasize new features in Spark 2.4., this second edition shows data engineers and scientists why structure and unification in Spark matters. Specifically, this book explains how to perform simple and complex data analytics and employ machine-learning algorithms.

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Book SynopsisGenerating functions, one of the most important tools in enumerative combinatorics, are a bridge between discrete mathematics and continuous analysis. Generating functions have numerous applications in mathematics, especially in - Combinatorics - Probability Theory - Statistics - Theory of Markov Chains - Number Theory One of the most important and relevant recent applications of combinatorics lies in the development of Internet search engines whose incredible capabilities dazzle even the mathematically trained user.Trade Review" ""Wilf's writing is clear and friendly; his exorcises are instructive and plentiful... This book is valuable reading for even the best of specialists..."" -E. Rodney Canfield, The Mathematical Intelligencer , March 1993 ""This is a first rate, carefully planned and executed book written by a 'black belt gereratingfunctionologist.' I'll be using it the next time I teach..."" -George Andrews, SIAM News, October 1994 ""Wilf's book is very well-written and easy to read by any serious mathematics student. Scientists in other disciplines often encounter the need to study sequences that naturally arise in their own discipline. The book is well-suited fo them, too."" -Short Book Reviews, January 2006"Table of ContentsIntroductory Ideas and Examples. Series. Cards, Decks and Hands: The Exponential Formula. Applications of Generating Functions. Analytic and Asymptotic Models. Appendix: Using Maple and Mathematica Solutions. References.

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