Preface......Page 3 Contents......Page 7 Part I Global Optimization......Page 17 Introduction......Page 19 Classification According to Method of Operation......Page 20 Classification According to Properties......Page 22 Single Objective Functions......Page 23 Multiple Objective Functions......Page 25 Weighted Sum......Page 27 Pareto Optimization......Page 29 The Method of Inequalities......Page 32 External Decision Maker......Page 34 Prevalence Optimization......Page 35 Spaces, Sets, and Elements......Page 37 Fitness Landscapes and Global Optimization......Page 45 Other General Features......Page 50 Premature Convergence and Multimodality......Page 53 Ruggedness and Weak Causality......Page 56 Neutrality and Redundancy......Page 59 Epistasis......Page 63 Overfitting and Oversimplification......Page 64 Robustness and Noise......Page 67 Dynamically Changing Fitness Landscape......Page 69 No Free Lunch Theorem......Page 70 Formae and Search Space/Operator Design......Page 71 Forma Analysis......Page 72 Genome Design......Page 74 Areas Of Application......Page 76 Conferences, Workshops, etc.......Page 77 Journals......Page 80 Books......Page 81 The Basic Principles from Nature......Page 83 Classification of Evolutionary Algorithms......Page 88 Areas Of Application......Page 92 Conferences, Workshops, etc.......Page 93 Online Resources......Page 96 Books......Page 97 Introduction......Page 98 Pareto Ranking......Page 99 Sharing Functions......Page 101 Variety Preserving Ranking......Page 103 Introduction......Page 108 Truncation Selection......Page 110 Fitness Proportionate Selection......Page 111 Tournament Selection......Page 116 Ordered Selection......Page 119 VEGA Selection......Page 121 Simple Convergence Prevention......Page 122 Reproduction......Page 124 NCGA Reproduction......Page 125 VEGA......Page 126 Introduction......Page 129 Areas Of Application......Page 130 Conferences, Workshops, etc.......Page 131 Books......Page 132 Genomes in Genetic Algorithms......Page 133 Mutation......Page 134 Crossover......Page 135 Crossover......Page 136 Schema Theorem......Page 137 Wildcards......Page 138 Criticism of the Schema Theorem......Page 139 The Building Block Hypothesis......Page 140 History......Page 141 Areas Of Application......Page 143 Conferences, Workshops, etc.......Page 144 Online Resources......Page 145 Creation......Page 146 Recombination......Page 147 Editing......Page 149 Wrapping......Page 150 Automatically Defined Functions......Page 151 Automatically Defined Macros......Page 152 Node Selection......Page 153 Cramer's Genetic Programming......Page 155 Gene Expression Programming......Page 156 Edge Encoding......Page 158 Trivial Approach......Page 161 Strongly Typed Genetic Programming......Page 162 Gads 1......Page 163 Grammatical Evolution......Page 165 Gads 2......Page 169 Christiansen Grammar Evolution......Page 170 Tree-Adjoining Grammar-guided Genetic Programming......Page 172 Linear Genetic Programming......Page 175 Parallel Algorithm Discovery and Orchestration......Page 177 Parallel Distributed Genetic Programming......Page 178 Genetic Network Programming......Page 180 Cartesian Genetic Programming......Page 181 Introduction......Page 183 Algorithmic Chemistry......Page 185 Rule-based Genetic Programming......Page 187 Soft Assignment......Page 193 Push, PushGP, and Pushpop......Page 194 Fraglets......Page 197 Restricting Problems......Page 200 Why No Exhaustive Testing?......Page 201 Non-Functional Features of Algorithms......Page 202 Areas Of Application......Page 205 (+)-ES......Page 206 Introduction......Page 207 General Information......Page 208 Areas Of Application......Page 209 Books......Page 210 Conferences, Workshops, etc.......Page 211 Messages......Page 212 Conditions......Page 214 Classifiers......Page 216 Non-Learning Classifier Systems......Page 217 The Bucket Brigade Algorithm......Page 218 Families of Learning Classifier Systems......Page 220 Introduction......Page 223 Multi-Objective Hill Climbing......Page 224 Problems in Hill Climbing......Page 225 Hill Climbing with Random Restarts......Page 226 Introduction......Page 227 Areas Of Application......Page 228 Introduction......Page 231 Temperature Scheduling......Page 233 Multi-Objective Simulated Annealing......Page 234 Areas Of Application......Page 237 The Downhill Simplex Algorithm......Page 238 Hybridizing with the Downhill Simplex......Page 240 Introduction......Page 243 Multi-Objective Tabu Search......Page 244 Introduction......Page 247 Online Resources......Page 248 Introduction......Page 251 Areas Of Application......Page 252 Books......Page 253 Memetic Algorithms......Page 255 Online Resources......Page 256 Books......Page 257 Introduction......Page 259 Breadth-First Search......Page 261 Depth-First Search......Page 262 Iterative Deepening Depth-First Search......Page 263 Random Walks......Page 264 Greedy Search......Page 265 Adaptive Walks......Page 266 Analysis......Page 269 Client-Server......Page 271 Island Model......Page 272 Cellular Genetic Algorithms......Page 275 Updating the Optimal Set......Page 277 Obtaining Optimal Elements......Page 278 Pruning the Optimal Set......Page 279 Adaptive Grid Archiving......Page 280 Part II Applications......Page 283 The Optimization Problem......Page 285 The Optimization Algorithm Applied......Page 286 Other Run Parameters......Page 287 Measures......Page 288 Simple Evaluation Measures......Page 289 Sophisticated Estimates......Page 291 Single-Objective Optimization......Page 293 Dynamic Fitness Landscapes......Page 294 Kauffman's NK Fitness Landscapes......Page 295 The p-Spin Model......Page 298 The Royal Road......Page 299 OneMax and BinInt......Page 303 Tunable Model for Problematic Phenomena......Page 304 Artificial Ant......Page 317 The Greatest Common Divisor......Page 319 Introduction......Page 331 The 2007 Contest -- Using Classifier Systems......Page 332 Introduction......Page 341 The 2006/2007 Semantic Challenge......Page 343 Genetic Programming: Genome for Symbolic Regression......Page 355 Sample Data, Quality, and Estimation Theory......Page 356 An Example and the Phenomenon of Overfitting......Page 357 Limits of Symbolic Regression......Page 359 Global Optimization of Distributed Systems......Page 361 Optimizing Network Topology and Dimensioning......Page 362 Optimizing Routing......Page 366 Synthesizing Protocols......Page 373 Optimizing Network Security......Page 377 Optimizing Parameters and Configurations......Page 378 Introduction......Page 379 Evolving Proactive Aggregation Protocols......Page 380 Part III Sigoa -- Implementation in Java......Page 403 Introduction......Page 405 Separation of Specification and Implementation......Page 406 Architecture......Page 407 Subsystems......Page 409 The Phenotype......Page 411 The Simulation......Page 412 The Objective Functions......Page 415 The Evolution Process......Page 417 Part IV Background......Page 419 Relations between Sets......Page 421 Operations on Sets......Page 422 Tuples......Page 424 Lists......Page 425 Binary Relations......Page 427 Functions......Page 428 Order Relations......Page 429 Equivalence Relations......Page 430 Probability......Page 431 Probabily as defined by Bernoulli (1713)......Page 432 The Axioms of Kolmogorov......Page 433 Conditional Probability......Page 434 Cumulative Distribution Function......Page 435 Probability Density Function......Page 436 Count, Min, Max and Range......Page 437 Variance and Standard Deviation......Page 438 Moments......Page 440 Median, Quantiles, and Mode......Page 441 Some Discrete Distributions......Page 443 Discrete Uniform Distribution......Page 444 Poisson Distribution......Page 445 Binomial Distribution B(n, p)......Page 448 Some Continuous Distributions......Page 449 Continuous Uniform Distribution......Page 450 Normal Distribution N(,2)......Page 451 Exponential Distribution exp()......Page 454 Chi-square Distribution......Page 455 Student's t-Distribution......Page 459 Example -- Throwing a Dice......Page 462 Estimation Theory......Page 464 Likelihood and Maximum Likelihood Estimators......Page 465 Confidence Intervals......Page 468 Density Estimation......Page 471 Generating Random Numbers......Page 473 Generating Pseudorandom Numbers......Page 474 Random Functions......Page 475 Converting Random Numbers to other Distributions......Page 476 Gamma Function......Page 480 Clustering......Page 481 Distance Measures for Real-Valued Vectors......Page 483 Cluster Error......Page 485 nth Nearest Neighbor Clustering......Page 486 Linkage Clustering......Page 487 Leader Clustering......Page 489 Algorithms and Programs......Page 493 Properties of Algorithms......Page 495 Complexity of Algorithms......Page 496 Randomized Algorithms......Page 498 Distributed Systems and Distributed Algorithms......Page 499 Network Topologies......Page 500 Some Architectures of Distributes Systems......Page 502 Modeling Distributed Systems......Page 507 Grammars and Languages......Page 515 Generative Grammars......Page 516 Derivation Trees......Page 517 Backus-Naur Form......Page 518 Attribute Grammars......Page 519 Extended Attribute Grammars......Page 521 Adaptive Grammars......Page 522 Christiansen Grammars......Page 523 Tree-Adjoining Grammars......Page 524 S-expressions......Page 526 Part V Appendices......Page 527 Abbreviations and Symbols......Page 529 Applicability and Definitions......Page 541 Verbatim Copying......Page 542 Modifications......Page 543 Translation......Page 545 Future Revisions of this License......Page 546 Preamble......Page 547 Terms and Conditions for Copying, Distribution and Modification......Page 548 No Warranty......Page 552 How to Apply These Terms to Your New Libraries......Page 553 Credits and Contributors......Page 555 Citation Suggestion......Page 557 References......Page 559 Index......Page 731 List of Figures......Page 747 List of Tables......Page 753 List of Algorithms......Page 755 List of Listings......Page 757

Harness the capabilities of Python and gain the expertise need to master data science techniques. This step-by-step book guides you through using Python to achieve tasks related to data cleaning, statistics, and visualization. You'll start by reviewing the foundational aspects of the data science process. This includes an extensive overview of research points and practical applications, such as the insightful analysis of presidential elections. The journey continues by navigating through installation procedures and providing valuable insights into Python, data types, typecasting, and essential libraries like Pandas and NumPy. You'll then delve into the captivating world of data visualization. Concepts such as scatter plots, histograms, and bubble charts come alive through detailed discussions and practical code examples, unraveling the complexities of creating compelling visualizations for enhanced data understanding. Statistical analysis, linear models, and advanced data preprocessing techniques are also discussed before moving on to preparing data for analysis, including renaming variables, variable rearrangement, and conditional statements. Finally, you'll be introduced to regression techniques, demystifying the intricacies of simple and multiple linear regression, as well as logistic regression. What You'll LearnUnderstand installation procedures and valuable insights into Python, data types, typecasting Examine the fundamental statistical analysis required in most data science and analytics reports Clean the most common data set problems Use linear progression for data prediction Who This Book Is ForData Analysts, data scientists, Python programmers, and software developers new to data science.

Solve design, planning, and control problems using modern machine learning and AI techniques. In Optimization Algorithms: AI techniques for design, planning, and control problems you will learn Machine learning methods for search and optimization problems The core concepts of search and optimization Deterministic and stochastic optimization techniques Graph search algorithms Nature-inspired search and optimization algorithms Efficient trade-offs between search space exploration and exploitation State-of-the-art Python libraries for search and optimization Optimization problems are everywhere in daily life. What’s the fastest route from one place to another? How do you calculate the optimal price for a product? How should you plant crops, allocate resources, and schedule surgeries? Optimization Algorithms introduces the AI algorithms that can solve these complex and poorly-structured problems. Inside you’ll find a wide range of optimization methods, from deterministic and stochastic derivative-free optimization to nature-inspired search algorithms and machine learning methods. Don’t worry—there’s no complex mathematical notation. You’ll learn through in-depth case studies that cut through academic complexity to demonstrate how each algorithm works in the real world. About the technology Search and optimization algorithms are powerful tools that can help practitioners find optimal or near-optimal solutions to a wide range of design, planning and control problems. When you open a route planning app, call for a rideshare, or schedule a hospital appointment, an AI algorithm works behind the scenes to make sure you get an optimized result. This guide reveals the classical and modern algorithms behind these services. About the book Optimization Algorithms: AI techniques for design, planning, and control problems explores the AI algorithms that determine the most efficient routes, optimal designs, and solve other logistical issues. Dive into the exciting world of classical problems like the Travelling Salesman Problem and the Knapsack Problem, as well as cutting-edge modern implementations like graph search methods, metaheuristics and machine learning. Discover how to use these algorithms in real-world situations, with in-depth case studies on assembly line balancing, fitness planning, rideshare dispatching, routing and more. Plus, get hands-on experience with practical exercises to optimize and scale the performance of each algorithm.

Make your public key protocols smaller and more secure with this accessible guide to Elliptic Curve Cryptography. Elliptic Curve Cryptography for Developers introduces the mathematics of elliptic curves—a powerful alternative to the prime number-based RSA encryption standard. You’ll learn to deliver zero-knowledge proofs and aggregated multi-signatures that are not even possible with RSA mathematics. All you need is the basics of calculus you learned in high school. Elliptic Curve Cryptography for Developers includes: • Clear, well-illustrated introductions to key ECC concepts • Implementing efficient digital signature algorithms • State of the art zero-knowledge proofs • Blockchain applications with ECC-backed security The book gradually introduces the concepts and subroutines you’ll need to master with diagrams, flow charts, and accessible language. Each chapter builds on what you’ve already learned, with step-by-step guidance until you’re ready to write embedded systems code with advanced mathematical algorithms. Purchase of the print book includes a free eBook in PDF and ePub formats from Manning Publications. About the technology The Elliptic Curve Cryptography (ECC) protocol secures everything from credit card transactions to the blockchain. With a little C code, high school calculus, and the techniques in this book, you can implement ECC cryptographic protocols that are smaller and more secure than the RSA-based systems in common use today. About the book Elliptic Curve Cryptography for Developers teaches you how ECC protocols work and how to implement them seamlessly in C code. Unlike academic cryptography books, this practical guide sticks to the minimum math and theory you need to get the job done. Author Mike Rosing illustrates each concept with clear graphics, detailed code, and hands-on exercises. As you go, you’ll practice what you learn by building two encryption systems for a blockchain application. What's inside • Efficient digital signature algorithms • Zero-knowledge proofs • ECC security for blockchain applications About the reader Readers need to understand basic calculus. Examples in C. About the author Michael Rosing ’s career as a scientist, hardware engineer, and software developer includes high-energy physics, telephone switch engineering, and developing vision devices for the blind. The technical editor on this book was Mark Bissen . Table of Contents 1 Pairings over elliptic curves in cryptography Part 1 2 Description of finite field mathematics 3 Explaining the core of elliptic curve mathematics 4 Key exchange using elliptic curves 5 Prime field elliptic curve digital signatures explained 6 Finding good cryptographic elliptic curves Part 2 7 Description of finite field polynomial math 8 Multiplication of polynomials explained 9 Computing powers of polynomials 10 Description of polynomial division using Euclid’s algorithm 11 Creating irreducible polynomials 12 Taking square roots of polynomials Part 3 13 Finite field extension curves described 14 Finding low embedding degree elliptic curves 15 General rules of elliptic curve pairing explained 16 Weil pairing defined 17 Tate pairing defined 18 Exploring BLS multi-signatures 19 Proving knowledge and keeping secrets: Zero knowledge using pairings Appendix A Code and tools Appendix B Hilbert class polynomials Appendix C Variables list

As there can be more than one algorithm for the same problem, designing and analyzing an algorithm becomes important in order to make it as efficient and robust as possible. This book will serve as a guide to design and analysis of computer algorithms. Chapter One provides an overview of different algorithm design techniques and the various applications of such techniques. Chapter Two reviews the divide and conquer strategy and the algorithm types that employ it. Chapter Three explores greedy algorithms and some problems that can be solved with this approach. Chapter Four discusses in depth the dynamic programming approach. Chapter Five provides a solution to the N-Queens problem utilizing a backtracking approach. Chapter Six elucidates the reader to branch and bound techniques and provides three solutions to problems implementing them. Part II of this book begins with Chapter Seven, where two different approaches to the analysis of algorithms are discussed. Chapter Eight reviews randomized algorithms through an empirical lens. Chapter Nine discusses Master Theorem and the many kinds of problems this Theorem can solve. Chapter Ten, the final chapter, provides notes on the empirical complexity analysis of algorithms.

Contents I Proofs Introduction 3 0.1 References 4 1 What is a Proof? 5 1.1 Propositions 5 1.2 Predicates 8 1.3 The Axiomatic Method 8 1.4 Our Axioms 9 1.5 Proving an Implication 11 1.6 Proving an “If and Only If” 13 1.7 Proof by Cases 15 1.8 Proof by Contradiction 16 1.9 Good Proofs in Practice 17 1.10 References 19 2 The Well Ordering Principle 29 2.1 Well Ordering Proofs 29 2.2 Template for Well Ordering Proofs 30 2.3 Factoring into Primes 32 2.4 Well Ordered Sets 33 3 Logical Formulas 45 3.1 Propositions from Propositions 46 3.2 Propositional Logic in Computer Programs 50 3.3 Equivalence and Validity 52 3.4 The Algebra of Propositions 55 3.5 The SAT Problem 60 3.6 Predicate Formulas 61 3.7 References 66 4 Mathematical Data Types 91 4.1 Sets 91 4.2 Sequences 96 4.3 Functions 97 4.4 Binary Relations 99 4.5 Finite Cardinality 103 5 Induction 123 5.1 Ordinary Induction 123 5.2 Strong Induction 132 5.3 Strong Induction vs. Induction vs. Well Ordering 139 6 State Machines 159 6.1 States and Transitions 159 6.2 The Invariant Principle 160 6.3 Partial Correctness & Termination 168 6.4 The Stable Marriage Problem 173 7 Recursive Data Types 203 7.1 Recursive Definitions and Structural Induction 203 7.2 Strings of Matched Brackets 207 7.3 Recursive Functions on Nonnegative Integers 211 7.4 Arithmetic Expressions 213 7.5 Induction in Computer Science 218 8 Infinite Sets 245 8.1 Infinite Cardinality 246 8.2 The Halting Problem 255 8.3 The Logic of Sets 259 8.4 Does All This Really Work? 262 II Structures Introduction 287 9 Number Theory 289 9.1 Divisibility 289 9.2 The Greatest Common Divisor 294 9.3 Prime Mysteries 301 9.4 The Fundamental Theorem of Arithmetic 303 9.5 Alan Turing 306 9.6 Modular Arithmetic 310 9.7 Remainder Arithmetic 312 9.8 Turing’s Code (Version 2.0) 315 9.9 Multiplicative Inverses and Cancelling 317 9.10 Euler’s Theorem 321 9.11 RSA Public Key Encryption 326 9.12 What has SAT got to do with it? 328 9.13 References 329 10 Directed graphs & Partial Orders 367 10.1 Vertex Degrees 369 10.2 Walks and Paths 370 10.3 Adjacency Matrices 373 10.4 Walk Relations 376 10.5 Directed Acyclic Graphs & Scheduling 377 10.6 Partial Orders 385 10.7 Representing Partial Orders by Set Containment 389 10.8 Linear Orders 390 10.9 Product Orders 390 10.10 Equivalence Relations 391 10.11 Summary of Relational Properties 393 11 Communication Networks 425 11.1 Routing 425 11.2 Routing Measures 426 11.3 Network Designs 429 12 Simple Graphs 445 12.1 Vertex Adjacency and Degrees 445 12.2 Sexual Demographics in America 447 12.3 Some Common Graphs 449 12.4 Isomorphism 451 12.5 Bipartite Graphs & Matchings 453 12.6 Coloring 458 12.7 Simple Walks 463 12.8 Connectivity 465 12.9 Forests & Trees 470 12.10 References 478 13 Planar Graphs 517 13.1 Drawing Graphs in the Plane 517 13.2 Definitions of Planar Graphs 517 13.3 Euler’s Formula 528 13.4 Bounding the Number of Edges in a Planar Graph 529 13.5 Returning to K5 and K3;3 530 13.6 Coloring Planar Graphs 531 13.7 Classifying Polyhedra 533 13.8 Another Characterization for Planar Graphs 536 III Counting Introduction 545 14 Sums and Asymptotics 547 14.1 The Value of an Annuity 548 14.2 Sums of Powers 554 14.3 Approximating Sums 556 14.4 Hanging Out Over the Edge 560 14.5 Products 566 14.6 Double Trouble 569 14.7 Asymptotic Notation 572 15 Cardinality Rules 597 15.1 Counting One Thing by Counting Another 597 15.2 Counting Sequences 598 15.3 The Generalized Product Rule 601 15.4 The Division Rule 605 15.5 Counting Subsets 608 15.6 Sequences with Repetitions 610 15.7 Counting Practice: Poker Hands 613 15.8 The Pigeonhole Principle 618 15.9 Inclusion-Exclusion 627 15.10 Combinatorial Proofs 633 15.11 References 637 16 Generating Functions 675 16.1 Infinite Series 675 16.2 Counting with Generating Functions 677 16.3 Partial Fractions 683 16.4 Solving Linear Recurrences 686 16.5 Formal Power Series 691 16.6 References 694 IV Probability Introduction 713 17 Events and Probability Spaces 715 17.1 Let’s Make a Deal 715 17.2 The Four Step Method 716 17.3 Strange Dice 725 17.4 The Birthday Principle 732 17.5 Set Theory and Probability 734 17.6 References 738 18 Conditional Probability 747 18.1 Monty Hall Confusion 747 18.2 Definition and Notation 748 18.3 The Four-Step Method for Conditional Probability 750 18.4 Why Tree Diagrams Work 752 18.5 The Law of Total Probability 760 18.6 Simpson’s Paradox 762 18.7 Independence 764 18.8 Mutual Independence 766 18.9 Probability versus Confidence 770 19 Random Variables 799 19.1 Random Variable Examples 799 19.2 Independence 801 19.3 Distribution Functions 802 19.4 Great Expectations 811 19.5 Linearity of Expectation 822 20 Deviation from the Mean 853 20.1 Markov’s Theorem 853 20.2 Chebyshev’s Theorem 856 20.3 Properties of Variance 860 20.4 Estimation by Random Sampling 866 20.5 Confidence in an Estimation 869 20.6 Sums of Random Variables 871 20.7 Really Great Expectations 880 21 Random Walks 905 21.1 Gambler’s Ruin 905 21.2 Random Walks on Graphs 915 V Recurrences Introduction 933 22 Recurrences 935 22.1 The Towers of Hanoi 935 22.2 Merge Sort 938 22.3 Linear Recurrences 942 22.4 Divide-and-Conquer Recurrences 949 22.5 A Feel for Recurrences 956 Bibliography 963 Glossary of Symbols 967 Index 971

This Handbook provides an interdisciplinary investigation into the role and influence of blockchain technology in areas such as the Metaverse, Non-Fungible Tokens (NFTs), tokenization, algorithmic governance, fraud and crime prevention. Drawing on cutting-edge research and analysis from leading experts in the field, it demystifies the complex nature of blockchain and its mechanisms, applications and potentials. Highlighting how blockchain technologies have disrupted traditional centralised systems, the Handbook analyses their influence on and transformation of many areas including finance, governance, ownership, digital assets, art and more. It examines the technological foundations of the Metaverse and the role of blockchain within it. Chapters explore the implications of NFTs and tokenization in art, finance and the Metaverse and review algorithmic governance and decentralised decision-making mechanisms. Adoption barriers and regulatory considerations are evaluated, highlighting real-world applications in various industries such as shipment transportation, crime prevention, financial investigation and next generation networks. The Handbook of Blockchain Technologyis a fundamental resource for academics, researchers and students of business and management or computer science, with a particular interest in disruptive technologies and blockchain, Metaverse, digital assets, information systems, and ICT. Business professionals and consultants in the technology sector will equally benefit from the book's practical application.

Get a detailed introduction to quantum computing and quantum machine learning, with a focus on finance-related applications Key FeaturesFind out how quantum algorithms enhance financial modeling and decision-making Improve your knowledge of the variety of quantum machine learning and optimisation algorithms Look into practical near-term applications for tackling real-world financial challenges Book Description As quantum machine learning (QML) continues to evolve, many professionals struggle to apply its powerful algorithms to real-world problems using noisy intermediate-scale quantum (NISQ) hardware. This book bridges that gap by focusing on hands-on QML applications tailored to NISQ systems, moving beyond the traditional textbook approaches that explore standard algorithms like Shor's and Grover's, which lie beyond current NISQ capabilities. You’ll get to grips with major QML algorithms that have been widely studied for their transformative potential in finance and learn hybrid quantum-classical computational protocols, the most effective way to leverage quantum and classical computing systems together. The authors, Antoine Jacquier, a distinguished researcher in quantum computing and stochastic analysis, and Oleksiy Kondratyev, a Quant of the Year awardee with over 20 years in quantitative finance, offer a hardware-agnostic perspective. They present a balanced view of both analog and digital quantum computers, delving into the fundamental characteristics of the algorithms while highlighting the practical limitations of today’s quantum hardware. By the end of this quantum book, you’ll have a deeper understanding of the significance of quantum computing in finance and the skills needed to apply QML to solve complex challenges, driving innovation in your work. What you will learn Familiarize yourself with analog and digital quantum computing principles and methods Explore solutions to NP-hard combinatorial optimisation problems using quantum annealers Build and train quantum neural networks for classification and market generation Discover how to leverage quantum feature maps for enhanced data representation Work with variational algorithms to optimise quantum processes Implement symmetric encryption techniques on a quantum computer Who this book is for This book is for academic researchers, STEM students, finance professionals in quantitative finance, and AI/ML experts. No prior knowledge of quantum mechanics is needed. Mathematical concepts are rigorously presented, but the emphasis is on understanding the fundamental properties of models and algorithms, making them accessible to a broader audience. With its deep coverage of QML applications for solving real-world financial challenges, this guide is an essential resource for anyone interested in finance and quantum computing.

The Deep Learning textbook is a resource intended to help students and practitioners enter the field of machine learning in general and deep learning in particular. The online version of the book is now complete and will remain available online for free. The print version will be available for sale soon.

Elevate your NLP journey with an immersive exploration into Python's rich ecosystem of toolkits and libraries. Tailored for those with a foundational understanding, this guide thrusts you into the heart of advanced NLP techniques, ensuring you gain mastery over the subject. Harness the Power of Python's Libraries: From NLTK to spaCy, from TextBlob to Gensim - we meticulously unpack the strengths, nuances, and applications of each library, empowering you to select and wield them with finesse. Deep Dives, Deeper Insights: Each chapter is designed to plunge you into specific toolkits, exploring their capabilities, advanced features, and potential use-cases. Demystify the complexities of these libraries, one function at a time. Practical Projects and Hands-on Exercises: Theory meets application in a harmonious blend of textual explanations paired with Python code. Tackle real-world challenges, derive actionable insights, and witness the transformative power of NLP. From Intermediate to Pro: Whether you're looking to bolster your NLP skills for professional projects or academic pursuits, this guide pushes the boundaries of your knowledge, elevating you from an intermediate enthusiast to an NLP pro. Collaborative Learning Environment: Benefit from a host of supplementary online resources, interactive Python notebooks, and collaborative forums. Engage, learn, share, and grow in a thriving community of like-minded individuals. "NLP with Python Book: A Middle-Level Guide To Deep Dive into Python's NLP Toolkits and Libraries" is more than just a guide—it's a comprehensive toolkit, mentor, and companion rolled into one. Embark on this transformative journey and harness the power of Python's vast NLP universe to its fullest potential.

This book is part of the CS 1800 coursework on Discrete Structures at Northeastern University, Boston. The book and the course introduces the mathematical structures and methods that form the foundation of computer science. The material will be motivated by applications from computer science. Students learn: (1) specific skills, e.g., binary and modular arithmetic, set notation, sequences, lists, trees, and graphs, etc.; (2) general knowledge, e.g., counting, proof, and analysis techniques needed to estimate the size of sets, the growth of functions, and the space-time complexity of algorithms; and (3) how to think, e.g., general problem solving techniques.

Introduction......Page 1 Theoretical foundations......Page 2 Modified fuzzy-rough sets......Page 3 Properties of proposed version offuzzy-rough sets......Page 5 Concluding remarks......Page 8 References......Page 9 Introduction......Page 10 Related works......Page 11 Wavelet-domain hidden Markov modelfor color images......Page 13 WD HMM methods......Page 15 Experiments and discussions......Page 16 Further reading......Page 19 Introduction......Page 20 Edge synthesis......Page 21 The set of test edges used......Page 24 Results......Page 26 Example of use......Page 28 Conclusions......Page 29 References......Page 30 Introduction......Page 31 Relation to prior work......Page 32 Image compression......Page 33 Cross correlation in compressed format......Page 34 Adaptive window positioning......Page 35 Fine correlation and complete depth map......Page 37 Real motion image pair......Page 39 References......Page 41 Introduction......Page 43 System overview......Page 44 Gabor filter design......Page 45 Gabor feature extraction......Page 46 Training sample collection......Page 47 Experimental results......Page 48 Comparison of performance......Page 50 References......Page 51 Introduction......Page 52 Binary texture analysis......Page 53 Run-length based features......Page 54 Spatial-size distribution related features......Page 55 Dimensionality reduction and clustering......Page 56 Experimental results......Page 57 References......Page 59 Introduction......Page 60 Music terminologies......Page 61 Main performance requirements of music melody stream mining......Page 62 Chord-set memory border......Page 63 MMSLMS-summary......Page 64 MMSLMS-mine......Page 68 Experimental results......Page 74 Conclusions......Page 75 References......Page 76 Introduction......Page 77 Classification and the K ndash L distance......Page 78 Application to feature selection......Page 80 Examples......Page 81 Feature space search......Page 82 Conclusion......Page 83 References......Page 84 Recursive computation method for fast encoding of vector quantization based on 2-pixel-merging sum pyramid data structure......Page 86 Introduction......Page 87 Previous work......Page 88 Proposed method......Page 89 Experimental results......Page 90 Conclusion......Page 91 References......Page 92 Design and implementation of a multi-PNN structure for discriminating one-month abstinent heroin addicts from healthy controls using the P600 component of ERP signals......Page 93 ERP generation procedure......Page 94 Compartmental classification......Page 95 Results and discussion......Page 97 References......Page 101 Introduction......Page 103 Texture directionality in multiple resolutions......Page 104 Texture local graylevel variability in multiple resolutions......Page 105 Experimental results......Page 107 References......Page 110 Introduction......Page 112 Global and contextual information......Page 114 Registration: obtaining invariance againstelastic transformations......Page 115 Similarity measure in the final comparison between images......Page 116 Performance of the correlograms......Page 117 Evaluation of the feedback scheme......Page 118 Computational cost: scaling the system......Page 120 Retrieval results......Page 121 Conclusions......Page 122 References......Page 123 Overview of some existing computational methods for the evaluation of symmetry......Page 124 Definition......Page 125 Characteristic point......Page 126 Properties of this parameter......Page 127 Properties of this parameter......Page 128 Improvements of the algorithms......Page 129 Conclusion......Page 130 References......Page 131 Introduction......Page 132 Conditional histograms......Page 134 Classification......Page 135 Segmentation......Page 136 Experimental data......Page 137 Classification results......Page 138 Segmentation......Page 140 References......Page 142 The setting......Page 144 The evidence......Page 145 The plot thickens......Page 146 First witness: Direct comparison......Page 147 Second witness: Overall accuracy......Page 148 Third witness: Inside Statlog......Page 149 The verdict......Page 150 References......Page 151 Density estimation for Bayesian network classifiers......Page 153 Non-parametric density estimation using kernels......Page 155 Spline-approximated KDE for BNCs......Page 156 The spline smoother......Page 157 Databases and methodology......Page 158 Sensitivity to spline order......Page 159 Sensitivity to dimensionality......Page 160 Discussion......Page 161 References......Page 162 Introduction......Page 164 Qualitative real-time range extraction for preplanned scene partitioning using laser beam coding......Page 166 Results......Page 167 Discussion......Page 172 References......Page 173 Introduction......Page 174 Coordinate systems......Page 176 Georeferencing for a push-broom line......Page 177 Resampling and interpolation......Page 178 Algorithm......Page 179 Check for the accuracy of rectification......Page 180 References......Page 183 05001558.PDF......Page 184

As the second book in the Anyone Can Code series, Algorithmic Thinking focuses on the logic behind computer programming and software design. With a data-centred approach, it starts with simple algorithms that work on simple data items and advances to more complex ones covering data structures and classes. Examples are given in C/C++ and Python and use both plain text and graphics applications to illustrate the concepts in different languages and forms. With the advances in Artificial Intelligence and automated code generators, it is essential to learn about the logic of what a code needs to do, not just how to write the code. Anyone Can Code: Algorithmic Thinking is suitable for anyone who aims to improve their programming skills and go beyond the simple craft of programming, stepping into the world of algorithm design. This book is independent of the first one in the series but assumes some basic familiarity with programming such as language syntax. Most code examples in this book are either in Python or C/C++, as they are very common and typical languages programmers use these days. Python and C/C++ examples are tested on Python version 3.7 and Microsoft Visual Studio 2019, respectively. C and C++ are two different languages, but C++ is considered an object-oriented extension to C. While almost any C program can be compiled with a C++ compiler, C++ offers new ways of doing things such as input/output and memory management, and there are some behind-the-scenes or syntax differences in the way C and C++ compilers work. For example, struct keyword is used in both languages to define a structure. In C, your structure cannot include functions but in C++ it can. C++ structures are basically classes, but their members are public by default. Also, in C, struct is not a regular type, so if you have defined one, to define a variable you still need to use the keyword struct.

Understanding the significance of data analytics is paramount for digital transformation but in many organizations they are separate units without fully aligned goals. As organizations are applying digital transformations to be adaptive and agile in a competitive environment, data analytics can play a critical role in their success. This book explores the crossroads between them and how to leverage their connection for improved business outcomes. The need to collaborate and share data is becoming an integral part of digital transformation. This not only creates new opportunities but also requires well-considered and continuously assessed decision-making as competitiveness is at stake. This book details approaches, concepts, and frameworks, as well as actionable insights and good practices, including combined data management and agile concepts. Critical issues are discussed such as data quality and data governance, as well as compliance, privacy, and ethics. It also offers insights into how both private and public organizations can innovate and keep up with growing data volumes and increasing technological developments in the short, mid, and long term. This book will be of direct appeal to global researchers and students across a range of business disciplines, including technology and innovation management, organizational studies, and strategic management. It is also relevant for policy makers, regulators, and executives of private and public organizations looking to implement successful transformation policies. Volumes of data are growing at an unprecedented speed, driven by the Internet of Things (IoT) and unstructured data (e.g. social media content), as well as additional data generated by transforming into digital organizations. This feeds back into data analytics as well as Data Science, requiring even more mature data management and governance to achieve enriched insights. In addition, the need to collaborate and share data is becoming an integral part of digital transformations. This not only creates new opportunities but also requires well-considered and continuously assessed decision-making as competitiveness is at stake. This book details approaches, concepts, and frameworks, as well as actionable insights and good practices, including combined data management and agile concepts. In addition, a deep dive into privacy and ethics will be included. Intuition and experience need to be powered by data analytics. Organizations need to integrate data-driven decision-making into their DNA. Data-driven decision-making is not limited to incremental (investment) decisions, it also extends to decision-making in day-to-day operations and processes. Improved incremental decision-making typically supports the more strategic decision-making by senior management and higher. Predictive and prescriptive Artificial Intelligence (AI) models predict future outcomes, enabling the decision maker to choose the best future course of action. Examples of data-driven algorithms are decision trees, support vector machines (SVM), K-means, k nearest neighbor (kNN), Adaboost, and Deep Learning (DL) algorithms. These will further drive innovation. This outlook is particularly relevant for decision-making in day-to-day processes; automatic pricing adjustments based on inventory, demand, and competitor pricing in retail would be good examples for data-driven analytics. In this book, data-driven analytics is embedded in data-driven decision-making.

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Unleash the Power of Words: Master Text Analysis with Natural Language Processing in Python In the age of information overload, text reigns supreme. From social media posts to scientific journals, the vast ocean of textual data holds invaluable insights waiting to be deciphered. Natural Language Processing (NLP) empowers you to do just that, transforming text into meaningful data and unlocking its hidden potential. This comprehensive guide, crafted for both beginners and seasoned programmers, equips you with the knowledge and tools to master text analysis with Python. Whether you're a data scientist seeking to extract valuable insights, a developer building intelligent applications, or simply someone fascinated by the power of language, this book is your gateway to the captivating world of NLP. Why choose this potent combination: TensorFlow, NLTK, and Keras? TensorFlow: Leverage the powerful computational capabilities of this open-source library, allowing you to tackle complex NLP tasks with ease. NLTK: Uncover the essential building blocks of NLP with this versatile toolkit, perfect for data pre-processing, text analysis, and feature engineering. Keras: Build efficient and scalable deep learning models with this user-friendly API, empowering you to unlock the full potential of NLP. What sets this book apart? Clear and concise explanations: Even if you're new to NLP or Python, this book breaks down complex concepts into bite-sized, easy-to-understand explanations. Hands-on learning: Dive right into practical projects, building real-world NLP applications like sentiment analysis tools, chatbots, and text summarization systems. Powerhouse libraries: Master the functionalities of TensorFlow, NLTK, and Keras, the "holy trio" of NLP in Python, and leverage their combined capabilities to conquer any text analysis challenge. In-depth exploration: Go beyond the basics and delve into advanced topics like named entity recognition, topic modeling, and machine translation, pushing the boundaries of your NLP expertise. Future-proof your skills: Stay ahead of the curve by exploring cutting-edge advancements** in NLP, including deep learning and natural language generation. Within these pages, you'll discover:The fundamentals of NLP: Grasp core concepts like text preprocessing, tokenization, and stemming, laying a solid foundation for your text analysis journey. Essential Python libraries: Master the functionalities of NLTK, spaCy, and TensorFlow, the powerhouses of Python-based NLP. Practical text analysis techniques: Learn how to clean, manipulate, and analyze text data, extracting valuable insights and uncovering hidden patterns. Building real-world NLP applications: Put your knowledge into action by crafting practical projects that address real-world challenges in various domains. A glimpse into the future: Explore the exciting possibilities of deep learning and natural language generation, preparing you for the ever-evolving landscape of NLP. This book is more than just a collection of information; it's a transformative journey. It empowers you to Unlock the secrets hidden within text data: Extract valuable insights from various sources, informing decision-making and driving innovation. Build intelligent applications: Craft chatbots, sentiment analysis tools, and other applications that revolutionize how we interact with machines. Become a sought-after NLP expert: Master a highly sought-after skill and position yourself at the forefront of technological advancement. Chapter 1 Pythonic Thinking and Libraries in Natural Language Processing (NLP) Data Structures and Algorithms for Natural Language Processing (NLP) Essential Python Modules for Natural Language Processing Chapter 2 Text Cleaning (Normalization, Tokenization, Stop Words, Stemming/Lemmatization) Regular Expressions for Text Manipulation: Vectorization Techniques (Word2Vec, GloVe, FastText) Feature Engineering for NLP Chapter 3 Working with Data Files and Libraries NLTK for Basic NLP Tasks (Tokenization, Tagging, Chunking, Parsing) Exploring TensorFlow Text and KerasNLP Chapter 4 Visualizing Text Data (Word Clouds, Frequency Distributions) Understanding Embeddings with t-SNE and PCA Chapter 5 Perceptrons and Multilayer Perceptrons (MLPs) Introduction to Gradient Descent and Backpropagation Convolutional Neural Networks (CNNs) for Text Classification Recurrent Neural Networks (RNNs) for Sequence Processing Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRUs) Chapter 6 Deep Learning Frameworks for NLP: TensorFlow Essentials for NLP (Datasets, Operations, Training) Keras for Building NLP Models (Layers, Sequential and Functional API) TensorFlow Text and KerasNLP (Tokenizers, Embeddings, Pre-trained Models) Chapter 7 Defining Loss Functions and Metrics (Accuracy, Precision, Recall, F1-Score) Regularization Techniques (Dropout, L1/L2 Regularization) Evaluation Strategies (Cross-Validation, Hyperparameter Tuning) Early Stopping and Model Checkpointing Chapter 8 Saving and Loading NLP Models (TensorFlow SavedModel, Keras HDF5) Web Application Development with Flask or Django API Development for NLP Services Chapter 9 Text Classification: Sentiment Analysis and Opinion Mining Topic Modeling and Text Clustering Spam Detection and Fake News Identification Chapter 10 Text Generation and Summarization: Language Modeling with LSTMs and Transformers Text Generation with Beam Search and Sampling Abstractive and Extractive Summarization Techniques Chapter 11 Question Answering and Dialogue Systems: Machine Reading Comprehension (MRC) with Recurrent Networks End-to-End Conversational Agents with Transformers Reinforcement Learning for Dialogue Management Chapter 12 Natural Language Understanding (NLU): Named Entity Recognition (NER) and Part-of-Speech (POS) Tagging Coreference Resolution and Semantic Role Labeling Relationship Extraction and Event Detection Chapter 13 Building a Chatbot with Rasa and TensorFlow: Rasa Framework Introduction Dialog Management and Intent Recognition Training and Deploying the Chatbot Chapter 14 Machine Translation with TensorFlow and NMT Models: Encoder-Decoder Architecture and Attention Mechanism Training a Translation Model on a Dataset Evaluating Translation Quality (BLEU, ROUGE) Chapter 15 Text-to-Speech (TTS) and Speech Recognition (ASR): TTS with Mel Spectrograms and WaveRNN ASR with DeepSpeech and Wav2Letter++ Building End-to-End Speech-Based Applications Chapter 16 Medical Text Analysis and Clinical Decision Support Financial Sentiment Analysis and Market Prediction NLP Applications in Other Domains (Social Media, Customer Service) Chapter 17 Best Practices for NLP Development: Data Quality and Augmentation Model Explainability and Interpretability Ethical Considerations and Bias Mitigation Chapter 18 NLP Datasets and Benchmarking Tools NLP Communities and Forums Conclusion

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Reactive Publishing Prescriptive Analytics: Prescribe with Python Harness the Power of Python to Make Optimal Decisions! 'Prescriptive Analytics: Prescribe with Python Programming' is the quintessential manual for professionals and enthusiasts eager to dive into the world of data-driven decision-making. This transformative text guides readers through the intricate landscape of Prescriptive Analytics using the versatility and accessibility of Python programming. Key Points - \*\*Practical Application:\*\* Unlike other analytical guides, this book emphasizes practical problem-solving and provides readers with real-world examples that can be implemented directly into their own business strategies. - \*\*Cutting-Edge Techniques:\*\* Learn the latest and most effective prescriptive analytics methodologies that combine machine learning, optimization, and simulation to extrapolate actionable insights and recommendations from your data. - \*\*Python Programming Focus:\*\* With its spotlight on Python, the industry's leading programming language for data science, this book ensures that readers are learning through tools that are both powerful and prevalent in the field. - \*\*Expert Guidance:\*\* Written by a seasoned professional with extensive experience in analytics, this book demystifies complex concepts and equips you with the know-how to embark on predictive and prescriptive projects with confidence. - \*\*Resource-Rich Experience:\*\* Gain access to a wealth of resources such as datasets, Python scripts, and additional reading materials to further your knowledge and skillset. Who is this for? - \*\*Data Science Practitioners:\*\* Analysts and data scientists looking to expand their expertise in prescriptive analytics and Python programming. - \*\*Business Decision-Makers:\*\* Executives and managers seeking to integrate data-driven decision-making within their strategic planning. - \*\*Academia:\*\* Students and educators desiring a comprehensive and applicable textbook for courses on analytics and decision science. - \*\*IT Professionals:\*\* Software developers and IT professionals aiming to incorporate prescriptive analytics into business intelligence tools. - \*\*DIY Enthusiasts:\*\* Self-learners and hobbyists hungry for a challenge and eager to apply prescriptive analytics in personal projects or potential startups. Catalyze your organization's decision processes, minimize risk, and unveil the most effective course of action with 'Prescriptive Analytics: Prescribe with Python'. Where data is plentiful but insights are scarce, let this book be your guide to turning complexities into calculated choices. Grab your copy today and start prescribing solutions with the power of Python!

AI-Powered Search teaches you the latest Machine Learning techniques to create search engines that continuously learn from your users and your content, to drive more domain-aware and intelligent search. Today’s search engines are expected to be smart, understanding the nuances of natural language queries, as well as each user’s preferences and context. AI-Powered Search is an authoritative guide to applying leading-edge Data Science techniques to search. As you can imagine given that goal, this is not an “introduction to search” book. In order to get the most out of this book, you should ideally already be familiar with the core capabilities of modern search engines (inverted indices, relevance ranking, faceting, query parsing, text analysis, and so on) through experience with a technology like Apache Solr, Elasticsearch/OpenSearch, Vespa, or Apache Lucene. If you need to come up to speed quickly, Solr in Action (which I also wrote) provides you with all the search background necessary to dive head-first into AI-Powered Search. Additionally, the code examples in this book are written in Python (and delivered in pre-configured Jupyter notebooks) to appeal both to engineers and data scientists. You don’t need to be an expert in Python, but you should have some programming experience to be able to read and understand the examples. Written by Trey Grainger, the Chief Algorithms Officer at Lucidworks, AI-Powered Search empowers you to create and deploy search engines that take advantage of user interactions. Working through code in interactive notebooks, you'll learn how to build search engines that automatically understand the intention of a query in order to deliver significantly better results. This book is an example-driven guide through the most applicable machine learning algorithms and techniques commonly leveraged to build intelligent search systems. We’ll not only walk through key concepts, but will also provide reusable code examples to cover data collection and processing techniques, as well as the self-learning query interpretation and relevance strategies employed to deliver AI-powered search capabilities across today’s leading organizations - hopefully soon to include your own!

Dive into over 90 captivating algorithm challenges, spanning more than 600 pages of insights and real-world applications. With comprehensive coverage of essential data structures like linked lists, stacks, queues, trees, and graphs, you'll be fully prepared for success in interviews and equipped to thrive at top tech companies worldwide Unlock your programming potential with "Data Structures & Algorithms for All Programmers." Gain invaluable knowledge, master implementation, and prepare for success in interviews while thriving in top tech companies worldwide. This captivating book offers More than 90 algorithm challenges. Over 600 pages of insights into algorithms and their real-world applications. Comprehensive coverage of essential data structures, including Arrays Linked lists Stacks Queues Trees Graphs

"Data Structure in Python: From Basics to Expert Proficiency" offers a comprehensive guide to understanding and implementing the core principles of data structures and algorithms using the Python programming language. Crafted for both beginners and experienced programmers, this book provides a clear and detailed exposition of essential data structures such as arrays, linked lists, stacks, queues, trees, and graphs. Each concept is meticulously explained with theoretical insights and practical Python implementations, ensuring a thorough grasp of the subject matter. Covering topics from fundamental algorithms to advanced data structures, this book emphasizes the importance of algorithm analysis, Big O notation, and performance optimization. Readers will benefit from the logical progression of topics, hands-on examples, and practical applications that reinforce learning. Whether you are looking to build a solid foundation in data structures or refine your expertise for complex problem-solving, this book serves as an invaluable resource in your journey towards mastering data structures and algorithms with Python.

Improve your app development skills by building a ChatGPT clone, code bug fixer, quiz generator, translation app, email auto-reply, PowerPoint generator, and more Key Features Transition into an expert AI developer by mastering ChatGPT concepts, including fine-tuning and integrations Gain hands-on experience through real-world projects covering a wide range of AI applications Implement payment systems in your applications by integrating the ChatGPT API with Stripe Purchase of the print or Kindle book includes a free PDF eBook Book Description Unlock the power of AI in your applications with ChatGPT with this practical guide that shows you how to seamlessly integrate OpenAI APIs into your projects, enabling you to navigate complex APIs and ensure seamless functionality with ease. This new edition is updated with key topics such as OpenAI Embeddings, which’ll help you understand the semantic relationships between words and phrases. You’ll find out how to use ChatGPT, Whisper, and DALL-E APIs through 10 AI projects using the latest OpenAI models, GPT-3.5, and GPT-4, with Visual Studio Code as the IDE. Within these projects, you’ll integrate ChatGPT with frameworks and tools such as Flask, Django, Microsoft Office APIs, and PyQt. You’ll get to grips with NLP tasks, build a ChatGPT clone, and create an AI code bug-fixing SaaS app. The chapters will also take you through speech recognition, text-to-speech capabilities, language translation, generating email replies, creating PowerPoint presentations, and fine-tuning ChatGPT, along with adding payment methods by integrating the ChatGPT API with Stripe. By the end of this book, you’ll be able to develop, deploy, and monetize your own groundbreaking applications by harnessing the full potential of ChatGPT APIs. What you will learn Develop a solid foundation in using the OpenAI API for NLP tasks Build, deploy, and integrate payments into various desktop and SaaS AI applications Integrate ChatGPT with frameworks such as Flask, Django, and Microsoft Office APIs Unleash your creativity by integrating DALL-E APIs to generate stunning AI art within your desktop apps Experience the power of Whisper API's speech recognition and text-to-speech features Find out how to fine-tune ChatGPT models for your specific use case Master AI embeddings to measure the relatedness of text strings Who this book is for This book is for a diverse range of professionals, including programmers, entrepreneurs, and software enthusiasts. Beginner programmers, Python developers exploring AI applications with ChatGPT, software developers integrating AI technology, and web developers creating AI-powered web applications with ChatGPT will find this book beneficial. Scholars and researchers working on AI projects with ChatGPT will also find it valuable. Basic knowledge of Python and familiarity with APIs is needed to understand the topics covered in this book.

One of the main problems in chip design is the enormous number of possible combinations of individual chip elements within a system, and the problem of their compatibility. The recent application of data structures, efficient algorithms, and ordered binary decision diagrams (OBDDs) has proven vital in designing the computer chips of tomorrow. This book provides an introduction to the foundations of this interdisciplinary research area, emphasizing its applications in computer aided circuit design.

What You Will Learn! The book begins by covering essential concepts and basic functions, ensuring readers have a solid foundation before diving into more advanced topics. It covers topics such as navigating the interface, formatting data, creating and editing tables, and performing calculations using various built-in functions. As readers progress through the book, they will learn about conditional formatting, data visualization techniques, and how to create charts and graphs to effectively present their findings. The book also explores the world of pivot tables, helping users analyze large datasets quickly and efficiently. In addition to these core features, "Master Excel: From Fundamentals to Advanced Techniques" delves into macros and VBA programming, enabling readers to automate repetitive tasks and develop custom solutions tailored to their unique needs. Throughout the book, real-world examples and practical tips are provided to reinforce key concepts and demonstrate the versatility of Excel in solving everyday problems across various industries. With its clear explanations, step-by-step instructions, and engaging content, "Master Excel: From Fundamentals to Advanced Techniques" empowers users to become true masters of Microsoft Excel. WHO EXCEL 2024 IS FOR? "Master Excel: From Fundamentals to Advanced Techniques" is a comprehensive guide that caters to both beginners and experienced users looking to expand their skills in Microsoft Excel. This book serves as a one-stop resource for anyone seeking to unlock the full potential of this powerful spreadsheet software. Unlock your full potential with Microsoft Excel – from fundamental basics to advanced techniques. This all-inclusive guide takes you on a journey through Excel's vast capabilities, equipping you with the knowledge needed to excel at work and beyond. Learn essential concepts, master formulas, discover data analysis tools, and dive into macro programming. Whether you’re new to Excel or a seasoned user, "Master Excel: From Fundamentals to Advanced Techniques" offers real-world examples, practical tips, and expert advice to elevate your Excel game. Upgrade your skillset and become a proficient Excel wizard today!

Abstract: The basic objective of digital defense is to verify the security intelligence of a distributed computing system (DCS) so that the information and communication technology assets of an enterprise are protected from various types of malicious attacks. DCS performs key functions to provide essential services and commodities such as defense, energy, utilities, transportation and communication system of a country. It is a part of a nation’s critical infrastructure and operates with the support of industrial control systems, supervisory control and data acquisition (SCADA), sensor networks, information and communication technologies. Distributed computing systems are potentially vulnerable to various types of malicious attacks which may affect the safety of common people and the performance of critical infrastructure seriously and may cause huge financial loss. The present work assesses the risk of different types of threats on DCS and presents a set of intelligent verification mechanisms which can protect the DCS from potential malicious attacks. The verification mechanisms are based on cryptography and distributed secure multi-party computation and check the security intelligence of the DCS from the perspectives of intrusion detection, secure communication, service oriented computing, credential based biometric access control, privacy and inference control. The work also explains the computational intelligence, simulation issues and key management associated with the verification mechanisms and suggests a roadmap for digital defense. An efficient DCS is expected to be a resilient system. The resiliency measures the ability to and the speed at which a DCS can return to normal performance level following a disruption. The vulnerability of a DCS to a disruptive event can be viewed as a combination of likelihood of a disruption and its potential severity.

Preface 1 Introduction 1.1 Painting and Drawing 1.2 Elements of 3D Graphics 1.3 Hardware and Software 2 Two-Dimensional Graphics 2.1 Pixels, Coordinates, and Colors 2.1.1 Pixel Coordinates 2.1.2 Real-number Coordinate Systems 2.1.3 Aspect Ratio 2.1.4 Color Models 2.2 Shapes 2.2.1 Basic Shapes 2.2.2 Stroke and Fill 2.2.3 Polygons, Curves, and Paths 2.3 Transforms 2.3.1 Viewing and Modeling 2.3.2 Translation 2.3.3 Rotation 2.3.4 Combining Transformations 2.3.5 Scaling 2.3.6 Shear 2.3.7 Window-to-Viewport 2.3.8 Matrices and Vectors 2.4 Hierarchical Modeling 2.4.1 Building Complex Objects 2.4.2 Scene Graphs 2.4.3 The Transform Stack 2.5 Java Graphics2D 2.5.1 Graphics2D 2.5.2 Shapes 2.5.3 Stroke and Fill 2.5.4 Transforms 2.5.5 BufferedImage and Pixels 2.6 HTML Canvas Graphics 2.6.1 The 2D Graphics Context 2.6.2 Shapes 2.6.3 Stroke and Fill 2.6.4 Transforms 2.6.5 Auxiliary Canvases 2.6.6 Pixel Manipulation 2.6.7 Images 2.7 SVG: A Scene Description Language 2.7.1 SVG Document Structure 2.7.2 Shapes, Styles, and Transforms 2.7.3 Polygons and Paths 2.7.4 Hierarchical Models 2.7.5 Animation 3 OpenGL 1.1: Geometry 3.1 Shapes and Colors in OpenGL 1.1 3.1.1 OpenGL Primitives 3.1.2 OpenGL Color 3.1.3 glColor and glVertex with Arrays 3.1.4 The Depth Test 3.2 3D Coordinates and Transforms 3.2.1 3D Coordinates 3.2.2 Basic 3D Transforms 3.2.3 Hierarchical Modeling 3.3 Projection and Viewing 3.3.1 Many Coordinate Systems 3.3.2 The Viewport Transformation 3.3.3 The Projection Transformation 3.3.4 The Modelview Transformation 3.3.5 A Camera Abstraction 3.4 Polygonal Meshes and glDrawArrays 3.4.1 Indexed Face Sets 3.4.2 glDrawArrays and glDrawElements 3.4.3 Data Buffers in Java 3.4.4 Display Lists and VBOs 3.5 Some Linear Algebra 3.5.1 Vectors and Vector Math 3.5.2 Matrices and Transformations 3.5.3 Homogeneous Coordinates 3.6 Using GLUT and JOGL 3.6.1 Using GLUT 3.6.2 Using JOGL 3.6.3 About glsim.js 4 OpenGL 1.1: Light and Material 4.1 Introduction to Lighting 4.1.1 Light and Material 4.1.2 Light Properties 4.1.3 Normal Vectors 4.1.4 The OpenGL 1.1 Lighting Equation 4.2 Light and Material in OpenGL 1.1 4.2.1 Working with Material 4.2.2 Defining Normal Vectors 4.2.3 Working with Lights 4.2.4 Global Lighting Properties 4.3 Image Textures 4.3.1 Texture Coordinates 4.3.2 MipMaps and Filtering 4.3.3 Texture Target and Texture Parameters 4.3.4 Texture Transformation 4.3.5 Loading a Texture from Memory 4.3.6 Texture from Color Buffer 4.3.7 Texture Objects 4.3.8 Loading Textures in C 4.3.9 Using Textures with JOGL 4.4 Lights, Camera, Action 4.4.1 Attribute Stack 4.4.2 Moving Camera 4.4.3 Moving Light 5 Three.js: A 3D Scene Graph API 5.1 Three.js Basics 5.1.1 Scene, Renderer, Camera 5.1.2 THREE.Object3D 5.1.3 Object, Geometry, Material 5.1.4 Lights 5.1.5 A Modeling Example 5.2 Building Objects 5.2.1 Polygonal Meshes and IFSs 5.2.2 Curves and Surfaces 5.2.3 Textures 5.2.4 Transforms 5.2.5 Loading Models 5.3 Other Features 5.3.1 Instanced Meshes 5.3.2 User Input 5.3.3 Shadows 5.3.4 Cubemap Textures and Skyboxes 5.3.5 Reflection and Refraction 6 Introduction to WebGL 6.1 The Programmable Pipeline 6.1.1 The WebGL Graphics Context 6.1.2 The Shader Program 6.1.3 Data Flow in the Pipeline 6.1.4 Values for Uniform Variables 6.1.5 Values for Attributes 6.1.6 Drawing a Primitive 6.1.7 WebGL 2.0: Vertex Array Objects 6.1.8 WebGL 2.0: Instanced Drawing 6.2 First Examples 6.2.1 WebGL Context Options 6.2.2 A Bit of GLSL 6.2.3 The RGB Triangle in WebGL 6.2.4 Shape Stamper 6.2.5 The POINTS Primitive 6.2.6 WebGL Error Handling 6.3 GLSL 6.3.1 Basic Types 6.3.2 Data Structures 6.3.3 Qualifiers 6.3.4 Expressions 6.3.5 Function Definitions 6.3.6 Control Structures 6.3.7 Limits 6.4 Image Textures 6.4.1 Texture Units and Texture Objects 6.4.2 Working with Images 6.4.3 More Ways to Make Textures 6.4.4 Cubemap Textures 6.4.5 A Computational Example 6.4.6 Textures in WebGL 2.0 6.5 Implementing 2D Transforms 6.5.1 Transforms in GLSL 6.5.2 Transforms in JavaScript 7 3D Graphics with WebGL 7.1 Transformations in 3D 7.1.1 About Shader Scripts 7.1.2 Introducing glMatrix 7.1.3 Transforming Coordinates 7.1.4 Transforming Normals 7.1.5 Rotation by Mouse 7.2 Lighting and Material 7.2.1 Minimal Lighting 7.2.2 Specular Reflection and Phong Shading 7.2.3 Adding Complexity 7.2.4 Two-sided Lighting 7.2.5 Moving Lights 7.2.6 Spotlights 7.2.7 Light Attenuation 7.2.8 Diskworld 2 7.3 Textures 7.3.1 Texture Transforms with glMatrix 7.3.2 Generated Texture Coordinates 7.3.3 Procedural Textures 7.3.4 Bumpmaps 7.3.5 Environment Mapping 7.4 Framebuffers 7.4.1 Framebuffer Operations 7.4.2 Render To Texture 7.4.3 Renderbuffers 7.4.4 Dynamic Cubemap Textures 7.5 WebGL Extensions 7.5.1 Anisotropic Filtering 7.5.2 Floating-Point Colors 7.5.3 Instanced Drawing in WebGL 1.0 7.5.4 Deferred Shading 7.5.5 Multiple Draw Buffers in WebGL 2.0 8 Beyond Basic 3D Graphics 8.1 Ray Tracing 8.1.1 Ray Casting 8.1.2 Recursive Ray Tracing 8.1.3 Limitations of Ray Tracing 8.2 Path Tracing 8.2.1 BSDF's 8.2.2 The Path Tracing Algorithm A Programming Languages A.1 The Java Programming Language A.1.1 Basic Language Structure A.1.2 Objects and Data Structures A.1.3 Windows and Events A.2 The C Programming Language A.2.1 Language Basics A.2.2 Pointers and Arrays A.2.3 Data Structures A.3 The JavaScript Programming Language A.3.1 The Core Language A.3.2 Arrays and Objects A.3.3 JavaScript on Web Pages A.3.4 Interacting with the Page B Blender B.1 Blender Basics B.1.1 The 3D View B.1.2 Adding Objects to the Scene B.1.3 Edit Mode B.1.4 Light, Material, and Texture B.1.5 Saving Your Work B.1.6 More Features B.2 Blender Modeling B.2.1 Text B.2.2 Curves B.2.3 Proportional Editing B.2.4 Extruding Meshes B.2.5 Mesh Modifiers B.3 Blender Animation B.3.1 Keyframe Animation and F-Curves B.3.2 Tracking B.3.3 Path Animation B.3.4 Rendering an Animation B.4 More on Light and Material B.4.1 Lighting B.4.2 Eevee versus Cycles B.4.3 The Shader Editor C Gimp and Inkscape C.1 Gimp: A 2D Painting Program C.1.1 Painting Tools C.1.2 Selections and Paths C.1.3 Layers C.2 Inkscape: A 2D Drawing Program D Listing of Sample Programs E Glossary

Unlock the Power of Text Analysis and Machine Learning with "Natural Language Processing with Python" In a world awash with data, the ability to harness the written word has become an indispensable skill. "Natural Language Processing with Python" is the key resource you need to elevate your data analysis repertoire to new heights. Building on fundamental Python programming skills, this book delves into the rich and complex field of Natural Language Processing (NLP) to unleash the full potential of textual data. Whether you're a professional data scientist yearning to master the nuances of NLP, or a Python-savvy analyst ready to tackle more challenging terrain, this comprehensive guide offers a blend of theoretical knowledge and practical examples that will allow you to - Implement sophisticated linguistic algorithms to parse speech, structure text, and understand meaning. - Employ advanced machine learning techniques to classify, extract, and interpret vast datasets with ease. - Develop a deeper understanding of text-based predictors and use them to forecast trends and behaviors. - Create engaging and intelligent chatbots that can interact naturally with users. - Master sentiment analysis to gauge public opinion and make informed decisions. Expanding on concepts that may have been introduced in prior best-sellers, "Natural Language Processing with Python" is keenly focused on equipping readers with the skills to tailor customized analytical tools for their unique professional needs. Packed with hands-on tutorials, clear explanations of complex algorithms, and real-world applications, this book is set to become an essential part of any data professional's library. Join us on a journey as we explore cutting-edge techniques such as deep learning, natural language generation, and multi-lingual processing. With this book, you will not only learn how to analyze text but also how to generate it—giving you the power to bring context and insight into every data

Broadcast or multicast is one of the most fundamental concepts in data communication and distributed cryptography. A central entity wishes to broadcast a secret data stream to a dynamically changing privileged subset of recipients in such a way that non-members of the privileged class cannot learn the secret. This work presents an Adaptively Secure Broadcast Algorithm (ASBA) based on threats analytics and case based reasoning. It defines the security intelligence of an adaptively secure broadcast comprehensively with a novel concept. It recommends a set of intelligent model checking moves for the verification of security intelligence of broadcasting mechanism. The algorithm is analyzed from the perspectives of security intelligence, communication complexity, computational intelligence and efficiency of mechanism. The computational intelligence is associated with the complexity of broadcast scheduling, verification of security intelligence of broadcasting system, key management strategies and payment function computation. The cost of communication depends on number of agents and subgroups in the broadcasting group and complexity of data. The algorithm is applicable to the analysis of intelligent mechanisms in static and dynamic networks, auction or combinatorial auction for e-market, digital content distribution through computational advertising, cloud computing, radio and digital TV broadcast, SCADA and sensor networks.

Make your public key protocols smaller and more secure with this accessible guide to Elliptic Curve Cryptography. Elliptic Curve Cryptography for Developers introduces the mathematics of elliptic curves—a powerful alternative to the prime number-based RSA encryption standard. You’ll learn to deliver zero-knowledge proofs and aggregated multi-signatures that are not even possible with RSA mathematics. All you need is the basics of calculus you learned in high school. Elliptic Curve Cryptography for Developers includes: • Clear, well-illustrated introductions to key ECC concepts • Implementing efficient digital signature algorithms • State of the art zero-knowledge proofs • Blockchain applications with ECC-backed security The book gradually introduces the concepts and subroutines you’ll need to master with diagrams, flow charts, and accessible language. Each chapter builds on what you’ve already learned, with step-by-step guidance until you’re ready to write embedded systems code with advanced mathematical algorithms. Purchase of the print book includes a free eBook in PDF and ePub formats from Manning Publications. About the technology The Elliptic Curve Cryptography (ECC) protocol secures everything from credit card transactions to the blockchain. With a little C code, high school calculus, and the techniques in this book, you can implement ECC cryptographic protocols that are smaller and more secure than the RSA-based systems in common use today. About the book Elliptic Curve Cryptography for Developers teaches you how ECC protocols work and how to implement them seamlessly in C code. Unlike academic cryptography books, this practical guide sticks to the minimum math and theory you need to get the job done. Author Mike Rosing illustrates each concept with clear graphics, detailed code, and hands-on exercises. As you go, you’ll practice what you learn by building two encryption systems for a blockchain application. What's inside • Efficient digital signature algorithms • Zero-knowledge proofs • ECC security for blockchain applications About the reader Readers need to understand basic calculus. Examples in C. About the author Michael Rosing ’s career as a scientist, hardware engineer, and software developer includes high-energy physics, telephone switch engineering, and developing vision devices for the blind. The technical editor on this book was Mark Bissen . Table of Contents 1 Pairings over elliptic curves in cryptography Part 1 2 Description of finite field mathematics 3 Explaining the core of elliptic curve mathematics 4 Key exchange using elliptic curves 5 Prime field elliptic curve digital signatures explained 6 Finding good cryptographic elliptic curves Part 2 7 Description of finite field polynomial math 8 Multiplication of polynomials explained 9 Computing powers of polynomials 10 Description of polynomial division using Euclid’s algorithm 11 Creating irreducible polynomials 12 Taking square roots of polynomials Part 3 13 Finite field extension curves described 14 Finding low embedding degree elliptic curves 15 General rules of elliptic curve pairing explained 16 Weil pairing defined 17 Tate pairing defined 18 Exploring BLS multi-signatures 19 Proving knowledge and keeping secrets: Zero knowledge using pairings Appendix A Code and tools Appendix B Hilbert class polynomials Appendix C Variables list

This book is a comprehensive exploration into the realm of sentiment analysis. From deciphering customer sentiments for businesses to understanding public opinions on social media or predicting market trends, the applications are multifaceted and impactful. Sentiment Analysis Unveiled: Techniques, Applications, and Innovations is more than just algorithms and models; it’s about unraveling the emotions, opinions, and perceptions encapsulated within the vast sea of textual data. This book explores topics from opinion mining, social media analysis, deep learning, security concerns, and healthcare systems, and it also delves into the ethical and legal implications of sentiment analysis. Through practical examples, case studies, and discussions on cutting‐edge innovations, the editors aim is to provide a holistic view that empowers you to navigate this field confidently. It involves the analysis of user‐generated content, deciphering sentiments expressed on platforms like Twitter and Facebook, and provides valuable insights into public opinion, brand perception, and emerging trends in the digital landscape. This book is intended for professionals, researchers, and scientists in the field of artificial intelligence and sentiments analysis; it will serve as a valuable resource for both beginners and experienced professionals in the field.

"Excel at Excel" is not just a workbook; it's a comprehensive and transformative guide meticulously designed to help individuals master Microsoft Excel, from the basics to advanced functionalities. Within its pages, readers embark on a journey of skill development, proficiency, and efficiency in using Excel for various tasks. This comprehensive guide is thoughtfully crafted to introduce and deepen the understanding of Excel's features and capabilities. It empowers readers to explore various exercises, examples, and practical applications that cover Excel's functionalities. The book offers structured guidance for individuals to become proficient in formulas, functions, data analysis, and visualization within Excel. At its core, "Excel at Excel" emphasizes the importance of hands-on learning, efficiency, and maximizing Excel's potential. It encourages readers to engage in exercises for mastering key Excel features, navigating data effectively, and applying advanced techniques such as pivot tables, macros, and data visualization tools for efficient work management. What sets this guide apart is its interactive nature, offering readers the opportunity to actively participate in learning Excel. It provides step-by-step tutorials, practice exercises, and real-life scenarios for applying Excel skills, empowering individuals to become proficient in utilizing Excel for professional and personal tasks. "Excel at Excel" isn't just a guidebook; it's a comprehensive companion on the transformative journey toward mastering Excel. It reassures individuals that, through intentional practice and the exploration of Excel's functionalities, they can enhance their skills and efficiency in utilizing Excel for diverse tasks and projects.

Introduction About This Book Warnings and Success Stories Who Should Read This Navigating the Book Chapters Conventions Used in This Book Big Data Why Is Big Data So Much More Complicated? Distributed Systems Are Hard What Does It All Mean, Basil? What Does It Mean for Software Engineering Teams? What Does It Mean for Data Warehousing Teams? What Is a Data Engineering Team? Skills Needed in a Team Skills Gap Analysis Skill Gap Analysis Results What I Look for in Data Engineering Teams Operations Quality Assurance What Is a Data Engineer? What I Look for in Data Engineers Qualified Data Engineers Not Just Data Warehousing and DBAs Ability Gap Themes and Thoughts of a Data Engineering Team Hub of the Wheel How to Work with a Data Science Team How to Work with a Data Warehousing Team How to Work with an Analytics and/or Business Intelligence Team “How I Evaluate Teams Equipment and Resources Thought Frameworks Building Data Pipelines Knowledge of Use Case Right Tool for the Job Crawl, Walk, Run Technologies Why Do Teams Fail? Why Do Teams Succeed? Paying the Piper Some Technologies Are Just Dead Ends What if You Have Gaps and Still Have to Do It? Pre-project Steps Use Case Evaluate the Team Choose the Technologies Write the Code Evaluate Repeat Probability of Success Conclusion Best Efforts About the Author

If you thought data structures and algorithms were all just theory, you're missing out on what they can do for your JavaScript 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. Every chapter features practice exercises to give you the hands-on information 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. The JavaScript...

Take your programming skills to the next level. Learn to build stacks, queues, trees, graphs, and efficient sorting and searching algorithms from scratch. Perhaps you’ve heard about Big O notation, stacks and queues, or bubble sort and quicksort. You’d like to learn more, but it’s hard to find any good examples and explanations that use your favorite programming language, Dart. Data Structures & Algorithms in Dart is here to help with in-depth explanations, copious illustrations, and step-by-step examples. The book begins by reviewing fundamental data structures like lists and maps and then goes on to teach you how to build other important structures from scratch, including stacks, linked lists, queues, trees and graphs. From there you’ll use these data structures to understand and write many different traversal, searching and sorting algorithms. All along the way, you’ll learn how to analyze the efficiency of your code and express that efficiency using Big O notation.

This book introduces the basic principles and implementation process of Deep Learning in a simple way, and uses Python's Numpy library to build its own Deep Learning library from scratch instead of using existing Deep Learning libraries. On the basis of introducing basic knowledge of Python programming, calculus, and probability statistics, the core basic knowledge of Deep Learning such as regression model, neural network, convolutional neural network, recurrent neural network, and generative network is introduced in sequence according to the development of Deep Learning. While analyzing the principle in a simple way, it provides a detailed code implementation process. It is like not teaching you how to use weapons and mobile phones, but teaching you how to make weapons and mobile phones by yourself. This book is not a tutorial on the use of existing Deep Learning libraries, but an analysis of how to develop Deep Learning libraries from 0. This method of combining the principle from 0 with code implementation can enable readers to better understand the basic principles of Deep Learning and the design ideas of popular Deep Learning libraries. Through reading this book, readers can follow step by step to build a deep learning library from 0 without any deep learning platform. Finally, as a comparison, the use of the Deep Learning platform Pytorch is introduced, so that readers can easily learn to use this deep learning platform, which will help readers understand the design ideas of these platforms more deeply, so as to better grasp and use these deep learning platforms. This book is suitable not only for beginners without any Deep Learning knowledge, but also for practitioners who have experience in using deep learning libraries and want to understand its underlying implementation principles. This book is especially suitable as a Deep Learning textbook for colleges and universities.

This popular book, updated as a textbook for classroom use, discusses text mining and different ways this type of data mining can be used to find implicit knowledge from text collections. The author provides the guidelines for implementing text mining systems in Java, as well as concepts and approaches. The book starts by providing detailed text preprocessing techniques and then goes on to provide concepts, the techniques, the implementation, and the evaluation of text categorization. It then goes into more advanced topics including text summarization, text segmentation, topic mapping, and automatic text management. The book features exercises and code to help readers quickly learn and apply knowledge.

One of the best things about OpenCV is that it comes with a lot of built-in primitives for image processing and computer vision operations. If you have to start from scratch and write something, you will need to define things like an image, a point, a rectangle, and so on. Almost every computer vision algorithm needs these. All of these basic structures are already built into OpenCV. They are all in the core module. Another benefit is that these frameworks are already optimized for speed and memory, so users don't have to bother about the specifics of implementation. The imgcodecs module is in charge of opening and saving image files. With a simple command, you can save the output image as either a jpg or a png file when you're done with it. When you work with cameras, you will have to deal with a lot of video files. There are different modules that take care of everything that has to do with putting and taking out video files. You can easily record a video from a webcam or read a video file in various formats. You can also set properties like frames per second, frame size, and so on to save a bunch of frames as a video file. Processes for handling images When you write a Computer Vision algorithm, you will use a lot of the same basic image processing steps over and over. The imgproc module has most of these functions. You can do things like image filtering, geometric transformations, morphological operations, drawing on images, color conversions, histograms, motion analysis, shape analysis, feature detection, and so on. In OpenCV, we only need one line to do many of these manipulatinos, as you would see in this OpenCV course.