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Here is your in-depth guide to cryptography and cryptanalysis in Java. This book includes challenging cryptographic solutions that are implemented in Java 21 and Jakarta EE 11. It provides a robust introduction to Java 21's new features and updates, a roadmap for Jakarta EE 11 security mechanisms, a unique presentation of the'hot points'(advantages and disadvantages) from the Java Cryptography Architecture (JCA), a new chapter on Quantum cryptography, and more. The book dives into the classical simple cryptosystems that form the basis of modern cryptography, with fully working solutions (encryption/decryption operations). Pseudo-random generators are discussed as well as real-life implementations. Hash functions are covered along with practical cryptanalysis methods and attacks, asymmetric and symmetric encryption systems, signature and identification schemes. The book wraps up with a presentation of lattice-based cryptography and the NTRU framework library. Modern encryption schemes for cloud and big data environments (homomorphic encryption and searchable encryption) also are included. After reading and using this book, you will be proficient with crypto algorithms and know how to apply them to problems you may encounter. New to This Edition: The modernized Second Edition is updated to reflect the latest language features in Java 21 and Jakarta 11, along with the introduction of a new chapter on Quantum Cryptography (Chapter 6). What You Will Learn Develop programming skills for writing cryptography algorithms in Java Dive into security schemes and modules using Java Explore “good” vs “bad” cryptography based on processing execution times and reliability Play with pseudo-random generators, hash functions, etc. Leverage lattice-based cryptography methods, the NTRU framework library, and more Who This Book Is For Those who want to learn and leverage cryptography and cryptanalysis using Java. Some prior Java and/or algorithm programming exposure is highly recommended.

This new and updated edition takes you through the details of machine learning to give you an understanding of cognitive computing, IoT, big data, AI, quantum computing, and more. The book explains how machine learning techniques are used to solve fundamental and complex societal and industry problems.This second edition builds upon the foundation of the first book, revises all of the chapters, and updates the research, case studies, and practical examples to bring the book up to date with changes that have occurred in machine learning. A new chapter on quantum computers and machine learning is included to prepare you for future challenges. Insights for decision makers will help you understand machine learning and associated technologies and make efficient, reliable, smart, and efficient business decisions. All aspects of machine learning are covered, ranging from algorithms to industry applications. Wherever possible, required practical guidelines and best practices related to machine learning and associated technologies are discussed. Also covered in this edition are hot-button topics such as ChatGPT, superposition, quantum machine learning, and reinforcement learning from human feedback (RLHF) technology. Upon completing this book, you will understand machine learning, IoT, and cognitive computing and be prepared to cope with future challenges related to machine learning.What You Will LearnMaster the essentials of machine learning, AI, cloud, and the cognitive computing technology stackUnderstand business and enterprise decision-making using machine learningBecome familiar with machine learning best practicesGain knowledge of quantum computing and quantum machine learningWho This Book Is ForManagers tasked with making key decisions who want to learn how and when machine learning and related technologies can help them

This book is a hands-on guide for programmers who want to learn how C++ is used to develop solutions for options and derivatives trading in the financial industry. It explores the main algorithms and programming techniques used in implementing systems and solutions for trading options and derivatives. This updated edition will bring forward new advances in C++ software language and libraries, with a particular focus on the new C++23 standard. The book starts by covering C++ language features that are frequently used to write financial software for options and derivatives. These features include the STL (standard template library), generic templates, functional programming, and support for numerical code. Examples include additional support for lambda functions with simplified syntax, improvements in automatic type detection for templates, custom literals, modules, constant expressions, and improved initialization strategies for C++ objects. This book also provides how-to examples that cover all the major tools and concepts used to build working solutions for quantitative finance. It discusses how to create bug-free and efficient applications, leveraging the knowledge of object-oriented and template-based programming. It has two new chapters covering backtesting option strategies and processing financial data.. It introduces the topics covered in the book in a logical and structured way, with lots of examples that will bring them to life. Options and Derivatives Programming in C++23 has been written with the goal of reaching readers who are looking for a concise, algorithms-based book that provides basic information through well-targeted examples and ready to use solutions. What You Will Learn Gain insight into the fundamental challenges of the options and derivatives marketMaster the features of the C++ language used in quantitative financial programmingUnderstand quantitative finance algorithms for options and derivativesBuild pricing algorithms around the Black-Scholes model, and use binomial and differential equations methods Who This Book Is For Professional developers who have some experience with the C++ language and would like to leverage that knowledge into financial software development.

Learning and using groundbreaking generative AI tools like ChatGPT and Google Bard can take some time. It's a journey that requires patience and flexibility. This book is an indispensable guide for any developer who wants to be a part of the ChatGPT and Bard revolution.ChatGPT and Bard represent an epochal event in the world of generative AI on par with the emergence of the Internet or the launch of the iPhone. But the impact could be even more transformative. No doubt, building applications with ChatGPT and Bard are quickly become strategic and a must-have. This is why developers must understand these technologies now.You'll start with a deep dive on ChatGPT and Bard and see how ChatGPT works with the transformer and diffusion models. The book also illustrates how to use the various APIs, such as GPT-4, ChatGPT, Bard, and Bedrock, focusing on the practical uses of these powerful technologies. This includes understanding embeddings, fine tuning ofthe models, using zero-shot and few-shot learning, and adjusting the parameters like temperature. ChatGPT and Bard for Business Automation is your step-by-step tutorial on how to create apps for blog generation, sentiment analysis, language translation and integration. What You Will Learn See how generative AI models work, their uses cases, benefits, and risks.Work with the various generative AI APIs such as GPT-4, ChatGPT, Bard, Bedrock and Anthropic.Understand prompt engineering, embeddings, fine tuning, and zero-shot/no-shot learning.Develop various applications, such as detecting sentiment. Create blogs and translate languages. Who This Book Is For Data scientists and engineers who want to leverage generative AI APIs; software engineers and developers interested in generative AI and how to integrate the APIs into existing IT environments; start-up personnel who want to use generative AI.

This book is an instructional text that will teach you how to code x86-64 assembly language functions. It also explains how you can exploit the SIMD capabilities of an x86-64 processor using x86-64 assembly language and the AVX, AVX2, and AVX-512 instruction sets.This updated edition's content and organization are designed to help you quickly understand x86-64 assembly language programming and the unique computational capabilities of x86 processors. The source code is structured to accelerate learning and comprehension of essential x86-64 assembly language programming constructs and data structures. Modern X86 Assembly Language Programming, Third Edition includes source code for both Windows and Linux. The source code elucidates current x86-64 assembly language programming practices, run-time calling conventions, and the latest generation of software development tools.What You Will LearnUnderstand important details of the x86-64 processor platform, including its core architecture, data types, registers, memory addressing modes, and the basic instruction setUse the x86-64 instruction set to create assembly language functions that are callable from C++Create assembly language code for both Windows and Linux using modern software development tools including MASM (Windows) and NASM (Linux)Employ x86-64 assembly language to efficiently manipulate common data types and programming constructs including integers, text strings, arrays, matrices, and user-defined structuresExplore indispensable elements of x86 SIMD architectures, register sets, and data types.Master x86 SIMD arithmetic and data operations using both integer and floating-point operandsHarness the AVX, AVX2, and AVX-512 instruction sets to accelerate the performance of computationally-intense calculations in machine learning, image processing, signal processing, computer graphics, statistics, and matrix arithmetic applicationsApply leading-edge coding strategies to optimally exploit the AVX, AVX2, and AVX-512 instruction sets for maximum possible performanceWho This Book Is ForSoftware developers who are creating programs for x86 platforms and want to learn how to code performance-enhanced algorithms using the core x86-64 instruction set; developers who need to learn how to write SIMD functions or accelerate the performance of existing code using the AVX, AVX2, and AVX-512 instruction sets; and computer science/engineering students or hobbyists who want to learn or better understand x86-64 assembly language programming and the AVX, AVX2, and AVX-512 instruction sets.

Advance your understanding of generic data structures and algorithms and their applications using Go and the effective use of concurrency. You are invited on a journey that aims to improve your programming and problem-solving skills. This book takes you to the next step by showing how to get your programs to work efficiently as well as correctly. As you explore many data structures and the algorithms and applications associated with them, you'll focus on the trade-offs between speed and storage and the benefits of deploying concurrency when appropriate. This book will demonstrate the huge increases in application performance that are possible. The presentation of classic data structures and techniques of algorithm design (greedy, divide and conquer, branch-and-bound to name a few) provides an essential foundation and toolkit for problem solving. But this book goes further by presenting heuristic algorithms and their implementations forsolving computationally intractable combinatoric optimization problems such as the travelling salesperson problem. Simulated annealing and genetic algorithms are among the techniques used.The consistent style of coding used throughout this book exploits Go's ability to implement abstract, generic and constrained generic data types without the use of classes. Although some familiarity with Go is assumed, this book should advance your ability to use Go to tackle server-side applications, games, machine learning, information retrieval and other application domains where speed and storage efficiency is essential.What You'll LearnExplore classical data structures and algorithms aimed at making your applications run faster or require less storageUse the new generic features of Go to build reusable data structuresUtilize concurrency for maximizing application performanceSee the power of heuristic algorithms for computationally intractable problemsEnhance and improve your Go programming skillsWho This Book Is ForPracticing Go software developers and students who wish to advance their programming and problem-solving skills and experience the excitement and see the benefits of using generic data structures and algorithms that utilize concurrency whenever possible.

Here is your in-depth guide to cryptography and cryptanalysis in Java. This book includes challenging cryptographic solutions that are implemented in Java 17 and Jakarta EE 10. It provides a robust introduction to Java 17's new features and updates, a roadmap for Jakarta EE 10 security mechanisms, a unique presentation of the'hot points'(advantages and disadvantages) from the Java Cryptography Architecture (JCA), and more. The book dives into the classical simple cryptosystems that form the basis of modern cryptography, with fully working solutions (encryption/decryption operations). Pseudo-random generators are discussed as well as real-life implementations. Hash functions are covered along with practical cryptanalysis methods and attacks, asymmetric and symmetric encryption systems, signature and identification schemes. The book wraps up with a presentation of lattice-based cryptography and the NTRU framework library. Modern encryption schemes for cloud and big data environments (homomorphic encryption and searchable encryption) also are included. After reading and using this book, you will be proficient with crypto algorithms and know how to apply them to problems you may encounter. What You Will Learn Develop programming skills for writing cryptography algorithms in Java Dive into security schemes and modules using Java Explore “good” vs “bad” cryptography based on processing execution times and reliability Play with pseudo-random generators, hash functions, etc. Leverage lattice-based cryptography methods, the NTRU framework library, and more Who This Book Is For Those who want to learn and leverage cryptography and cryptanalysis using Java. Some prior Java and/or algorithm programming exposure is highly recommended.

Learn approaches of computational thinking and the art of designing algorithms. Most of the algorithms you will see in this book are used in almost all software that runs on your computer. Learning how to program can be very rewarding. It is a special feeling to seeing a computer translate your thoughts into actions and see it solve your problems for you. To get to that point, however, you must learn to think about computations in a new way—you must learn computational thinking. This book begins by discussing models of the world and how to formalize problems. This leads onto a definition of computational thinking and putting computational thinking in a broader context. The practical coding in the book is carried out in Python; you'll get an introduction to Python programming, including how to set up your development environment. What You Will LearnThink in a computational wayAcquire general techniques for problem solvingSeegeneral and concrete algorithmic techniquesProgram solutions that are both computationally efficient and maintainable Who This Book Is For Those new to programming and computer science who are interested in learning how to program algorithms and working with other computational aspects of programming.

'It's a joy to read a book by a mathematician who knows how to write. [...] There is no better guide to the strategies and stakes of this battle for the future.'---Paul Romer, Nobel Laureate, University Professor in Economics at NYU, and former Chief Economist at the World Bank. “By explaining the flaws and foibles of everything from Google search to QAnon—and by providing level-headed evaluations of efforts to fix them—Noah Giansiracusa offers the perfect starting point for anyone entering the maze of modern digital media.”—Jonathan Rauch, senior fellow at the Brookings Institute and contributing editor of The AtlanticFrom deepfakes to GPT-3, deep learning is now powering a new assault on our ability to tell what's real and what's not, bringing a whole new algorithmic side to fake news. On the other hand, remarkable methods are being developed to help automate fact-checking and the detection of fake news and doctored media. Success in the modern business world requires you to understand these algorithmic currents, and to recognize the strengths, limits, and impacts of deep learning---especially when it comes to discerning the truth and differentiating fact from fiction. This book tells the stories of this algorithmic battle for the truth and how it impacts individuals and society at large. In doing so, it weaves together the human stories and what's at stake here, a simplified technical background on how these algorithms work, and an accessible survey of the research literature exploring these various topics. How Algorithms Create and Prevent Fake News is an accessible, broad account of the various ways that data-driven algorithms have been distorting reality and rendering the truth harder to grasp. From news aggregators to Google searches to YouTube recommendations to Facebook news feeds, the way we obtain information todayis filtered through the lens of tech giant algorithms. The way data is collected, labelled, and stored has a big impact on the machine learning algorithms that are trained on it, and this is a main source of algorithmic bias ­– which gets amplified in harmful data feedback loops. Don't be afraid: with this book you'll see the remedies and technical solutions that are being applied to oppose these harmful trends. There is hope.What You Will LearnThe ways that data labeling and storage impact machine learning and how feedback loops can occurThe history and inner-workings of YouTube's recommendation algorithmThe state-of-the-art capabilities of AI-powered text generation (GPT-3) and video synthesis/doctoring (deepfakes) and how these technologies have been used so farThe algorithmic tools available to help with automated fact-checking and truth-detectionWho This Book is ForPeople who don't have a technical background (in data, computers, etc.) but who would like to learn how algorithms impact society; business leaders who want to know the powers and perils of relying on artificial intelligence. A secondary audience is people with a technical background who want to explore the larger social and societal impact of their work.

Master algorithms programming using Lisp, including the most important data structures and algorithms. This book also covers the essential tools that help in the development of algorithmic code to give you all you need to enhance your code.Programming Algorithms in Lisp shows real-world engineering considerations and constraints that influence the programs that use these algorithms. It includes practical use cases of the applications of the algorithms to a variety of real-world problems. What You Will LearnProgram algorithms using the Lisp programming languageWork with data structures, arrays, key-values, hash-tables, trees, graphs, and moreUse dynamic programming Program using stringsWork with approximations and compression Who This Book Is For Intermediate Lisp programmers wanting to do algorithms programming. A very experienced non-Lisp programmer may beable to benefit from this book as well.

Master the features of C++ that are frequently used to write financial software for options and derivatives, including the STL, templates, functional programming, and numerical libraries. This book also covers new features introduced in C++20 and other recent standard releases: modules, concepts, spaceship operators, and smart pointers. You will explore how-to examples covering all the major tools and concepts used to build working solutions for quantitative finance. These include advanced C++ concepts as well as the basic building libraries used by modern C++ developers, such as the STL and Boost, while also leveraging knowledge of object-oriented and template-based programming. Options and Derivatives Programming in C++ provides a great value for readers who are trying to use their current programming knowledge in order to become proficient in the style of programming used in large banks, hedge funds, and other investment institutions. The topics covered in the book are introduced in a logical and structured way and even novice programmers will be able to absorb the most important topics and competencies. This book is written with the goal of reaching readers who need a concise, algorithms-based book, providing basic information through well-targeted examples and ready-to-use solutions. You will be able to directly apply the concepts and sample code to some of the most common problems faced in the analysis of options and derivative contracts. What You Will Learn Discover how C++ is used in the development of solutions for options and derivatives trading in the financial industry Grasp the fundamental problems in options and derivatives trading Converse intelligently about credit default swaps, Forex derivatives, and more Implement valuation models and trading strategies Build pricing algorithms around the Black-Sholesmodel, and also using the binomial and differential equations methods Run quantitative finance algorithms using linear algebra techniques Recognize and apply the most common design patterns used in options trading Who This Book Is For Professional developers who have some experience with the C++ language and would like to leverage that knowledge into financial software development.

Implement practical data structures and algorithms for text search and discover how it is used inside other larger applications. This unique in-depth guide explains string algorithms using the C programming language. String Algorithms in C teaches you the following algorithms and how to use them: classical exact search algorithms; tries and compact tries; suffix trees and arrays; approximative pattern searches; and more. In this book, author Thomas Mailund provides a library with all the algorithms and applicable source code that you can use in your own programs. There are implementations of all the algorithms presented in this book so there are plenty of examples. You'll understand that string algorithms are used in various applications such as image processing, computer vision, text analytics processing from data science to web applications, information retrieval from databases, network security, and much more. What You Will Learn Use classical exact search algorithms including naive search, borders/border search, Knuth-Morris-Pratt, and Boyer-Moor with or without Horspool Search in trees, use tries and compact tries, and work with the Aho-Carasick algorithm Process suffix trees including the use and development of McCreight's algorithm Work with suffix arrays including binary searches; sorting naive constructions; suffix tree construction; skew algorithms; and the Borrows-Wheeler transform (BWT) Deal with enhanced suffix arrays including longest common prefix (LCP) Carry out approximative pattern searches among suffix trees and approximative BWT searches Who This Book Is For Those with at least some prior programming experience with C or Assembly and have at least prior experience with programming algorithms.

Study elementary and complex algorithms with clear examples and implementations in C. This book introduces data types (simple and structured) and algorithms with graphical and textual explanations. In the next sections, you'll cover simple and complex standard algorithms with their flowcharts: everything is integrated with explanations and tables to give a step-by-step evolution of the algorithms. The main algorithms are: the sum of three or n numbers in a loop, decimal-to-binary conversion, maximum and minimum search, linear/sequential search, binary search, bubble sort, selection sort, merging of two sorted arrays, reading characters from a file, stack management, and factorial and Fibonacci sequences. The last section of Introducing Algorithms in C is devoted to the introduction of the C language and the implementation of the code, which is connected to the studied algorithms. The book is full of screenshots and illustrations showingthe meaning of the code. What You Will Learn Implement algorithms in C Work with variables, constants, and primitive and structured types Use arrays, stacks, queues, graphs, trees, hash tables, records, and files Explore the design of algorithms Solve searching problems, including binary search, sorting, and bubble/selection sort Program recursive algorithms with factorial functions and Fibonacci sequences Who This Book Is For Primarily beginners: it can serve as a starting point for anyone who is beginning the study of computer science and information systems for the first time.

In the era of self-taught developers and programmers, essential topics in the industry are frequently learned without a formal academic foundation. A solid grasp of data structures and algorithms (DSA) is imperative for anyone looking to do professional software development and engineering, but classes in the subject can be dry or spend too much time on theory and unnecessary readings. Regardless of your programming language background, Codeless Data Structures and Algorithms has you covered.In this book, author Armstrong Subero will help you learn DSAs without writing a single line of code. Straightforward explanations and diagrams give you a confident handle on the topic while ensuring you never have to open your code editor, use a compiler, or look at an integrated development environment. Subero introduces you to linear, tree, and hash data structures and gives you important insights behind the most common algorithms that youcan directly apply to your own programs. Codeless Data Structures and Algorithms provides you with the knowledge about DSAs that you will need in the professional programming world, without using any complex mathematics or irrelevant information. Whether you are a new developer seeking a basic understanding of the subject or a decision-maker wanting a grasp of algorithms to apply to your projects, this book belongs on your shelf. Quite often, a new, refreshing, and unpretentious approach to a topic is all you need to get inspired. What You'll LearnUnderstand tree data structures without delving into unnecessary details or going into too much theoryGet started learning linear data structures with a basic discussion on computer memory Study an overview of arrays, linked lists, stacks and queuesWho This Book IsForThis book is for beginners, self-taught developers and programmers, and anyone who wants to understand data structures and algorithms but don't want to wade through unnecessary details about quirks of a programming language or don't have time to sit and read a massive book on the subject. This book is also useful for non-technical decision-makers who are curious about how algorithms work.

Build programs that give users full control of their applications in order to meet end users'unique needs and scenarios. Over the last couple of decades, there has been an ongoing quandary in the developer world. Developers are enlisted to build applications to meet users'demands; users get applications that meet the criteria from the developers'point of view, but they are far from what the users envisioned. The difference is often wide and nearly catastrophic in fields where users'actions are nearly impossible to predict, such as science, research work, financial analysis, and others. End users get frustrated with the applications because they were not built with their use cases in mind. For a long time, it was assumed that the developers who created the code should drive their programs and be responsible for all scenarios. While generally not an issue in simple programs, this view is wrong for complex applications in the field of science. These end users are the best specialists in their respective fields and need applications to work beyond the scenarios prepared and allowed for by developers.This book teaches you methods to manage your applications in a way that gives control to your target end users. You will learn proven methods using an easy and predictable instrument, the all-powerful algorithm, to create objects that are movable and re-sizable by users.Get ready to learn by example, using an algorithm of total movability and experience, implemented in different situations. You will begin with the simplest code examples and work your way up to real, complicated programs applicable in STEM fields.What You Will Learn Pass the control of your programs from developers to end usersUnderstand that the most valuable result is not the algorithm itself, but the consequence of using itBuild user-driven applications that include total movability of screen elementsSee concepts applied in real situations and scenariosBe exposed to well-known programs and tasks for developing user-driven applicationsAccess accompanying code written in C# and available on GitHub Who This Book Is ForDevelopers who want to write or design programs that give their target end users full control over their application

This open access book is a modern guide for all C++ programmers to learn Threading Building Blocks (TBB). Written by TBB and parallel programming experts, this book reflects their collective decades of experience in developing and teaching parallel programming with TBB, offering their insights in an approachable manner. Throughout the book the authors present numerous examples and best practices to help you become an effective TBB programmer and leverage the power of parallel systems. Pro TBB starts with the basics, explaining parallel algorithms and C++'s built-in standard template library for parallelism. You'll learn the key concepts of managing memory, working with data structures and how to handle typical issues with synchronization. Later chapters apply these ideas to complex systems to explain performance tradeoffs, mapping common parallel patterns, controlling threads and overhead, and extending TBB to program heterogeneous systems or system-on-chips. What You'll LearnUse Threading Building Blocks to produce code that is portable, simple, scalable, and more understandableReview best practices for parallelizing computationally intensive tasks in your applicationsIntegrate TBB with other threading packagesCreate scalable, high performance data-parallel programsWork with generic programming to write efficient algorithmsWho This Book Is ForC++ programmers learning to run applications on multicore systems, as well as C or C++ programmers without much experience with templates. No previous experience with parallel programming or multicore processors is required.

Human attention is in the highest demand it has ever been. The drastic increase in available information has compelled individuals to find a way to sift through the media that is literally at their fingertips. Content recommendation systems have emerged as the technological solution to this social and informational problem, but they've also created a bigger crisis in confirming our biases by showing us only, and exactly, what it predicts we want to see. Data versus Democracy investigates and explores how, in the era of social media, human cognition, algorithmic recommendation systems, and human psychology are all working together to reinforce (and exaggerate) human bias. The dangerous confluence of these factors is driving media narratives, influencing opinions, and possibly changing election results. In this book, algorithmic recommendations, clickbait, familiarity bias, propaganda, and other pivotal concepts are analyzed and then expanded upon via fascinating and timely case studies: the 2016 US presidential election, Ferguson, GamerGate, international political movements, and more events that come to affect every one of us. What are the implications of how we engage with information in the digital age? Data versus Democracy explores this topic and an abundance of related crucial questions. We live in a culture vastly different from any that has come before. In a society where engagement is currency, we are the product. Understanding the value of our attention, how organizations operate based on this concept, and how engagement can be used against our best interests is essential in responsibly equipping ourselves against the perils of disinformation.Who This Book Is ForIndividuals who are curious about how social media algorithms work and how they can be manipulated to influence culture. Social media managers, data scientists, data administrators, and educators will find this book particularly relevant to their work.

Are algorithms friend or foe?The human mind is evolutionarily designed to take shortcuts in order to survive. We jump to conclusions because our brains want to keep us safe. A majority of our biases work in our favor, such as when we feel a car speeding in our direction is dangerous and we instantly move, or when we decide not take a bite of food that appears to have gone bad. However, inherent bias negatively affects work environments and the decision-making surrounding our communities. While the creation of algorithms and machine learning attempts to eliminate bias, they are, after all, created by human beings, and thus are susceptible to what we call algorithmic bias.In Understand, Manage, and Prevent Algorithmic Bias, author Tobias Baer helps you understand where algorithmic bias comes from, how to manage it as a business user or regulator, and how data science can prevent bias from entering statistical algorithms. Baer expertly addresses someof the 100+ varieties of natural bias such as confirmation bias, stability bias, pattern-recognition bias, and many others. Algorithmic bias mirrors—and originates in—these human tendencies. Baer dives into topics as diverse as anomaly detection, hybrid model structures, and self-improving machine learning. While most writings on algorithmic bias focus on the dangers, the core of this positive, fun book points toward a path where bias is kept at bay and even eliminated. You'll come away with managerial techniques to develop unbiased algorithms, the ability to detect bias more quickly, and knowledge to create unbiased data. Understand, Manage, and Prevent Algorithmic Bias is an innovative, timely, and important book that belongs on your shelf. Whether you are a seasoned business executive, a data scientist, or simply an enthusiast, now is a crucial time to be educated about the impact of algorithmic bias on society and take an active role in fighting bias.What You'll LearnStudy the many sources of algorithmic bias, including cognitive biases in the real world, biased data, and statistical artifactUnderstand the risks of algorithmic biases, how to detect them, and managerial techniques to prevent or manage themAppreciate how machine learning both introduces new sources of algorithmic bias and can be a part of a solutionBe familiar with specific statistical techniques a data scientist can use to detect and overcome algorithmic biasWho This Book is ForBusiness executives of companies using algorithms in daily operations; data scientists (from students to seasoned practitioners) developing algorithms; compliance officials concerned about algorithmic bias; politicians, journalists, and philosophers thinking about algorithmic bias in terms of its impact on society and possible regulatory responses;and consumers concerned about how they might be affected by algorithmic bias

Gain the fundamentals of x86 64-bit assembly language programming and focus onthe updated aspects of the x86 instruction set that are most relevant to applicationsoftware development. This book covers topics including x86 64-bit programming andAdvanced Vector Extensions (AVX) programming. The focus in this second edition is exclusively on 64-bit base programming architectureand AVX programming. Modern X86 Assembly Language Programming's structure andsample code are designed to help you quickly understand x86 assembly languageprogramming and the computational capabilities of the x86 platform. After readingand using this book, you'll be able to code performance-enhancing functions andalgorithms using x86 64-bit assembly language and the AVX, AVX2 and AVX-512 instruction set extensions.What You Will LearnDiscover details of the x86 64-bit platform including its core architecture, data types,registers, memory addressing modes, and the basic instruction setUse the x86 64-bit instruction set to create performance-enhancing functions thatare callable from a high-level language (C++)Employ x86 64-bit assembly language to efficiently manipulate common data typesand programming constructs including integers, text strings, arrays, and structuresUse the AVX instruction set to perform scalar floating-point arithmeticExploit the AVX, AVX2, and AVX-512 instruction sets to significantly accelerate theperformance of computationally-intense algorithms in problem domains such asimage processing, computer graphics, mathematics, and statisticsApply various coding strategies and techniques to optimally exploit the x86 64-bit,AVX, AVX2, and AVX-512 instruction sets for maximum possible performanceWho This Book Is For Software developers who want to learn how to write code using x86 64-bit assembly language. It's also ideal for software developers who already have a basic understanding of x86 32-bit or 64-bit assembly language programming and are interested in learning how to exploit the SIMD capabilities of AVX, AVX2 and AVX-512.

Discover hidden relationships among the variables in your data, and learn how to exploit these relationships. This book presents a collection of data-mining algorithms that are effective in a wide variety of prediction and classification applications. All algorithms include an intuitive explanation of operation, essential equations, references to more rigorous theory, and commented C++ source code.Many of these techniques are recent developments, still not in widespread use. Others are standard algorithms given a fresh look. In every case, the focus is on practical applicability, with all code written in such a way that it can easily be included into any program. The Windows-based DATAMINE program lets you experiment with the techniques before incorporating them into your own work.What You'll LearnUse Monte-Carlo permutation tests to provide statistically sound assessments of relationships present in your dataDiscover how combinatorially symmetric cross validation reveals whether your model has true power or has just learned noise by overfitting the dataWork with feature weighting as regularized energy-based learning to rank variables according to their predictive power when there is too little data for traditional methodsSee how the eigenstructure of a dataset enables clustering of variables into groups that exist only within meaningful subspaces of the dataPlot regions of the variable space where there is disagreement between marginal and actual densities, or where contribution to mutual information is highWho This Book Is ForAnyone interested in discovering and exploiting relationships among variables. Although all code examples are written in C++, the algorithms are described in sufficient detail that they can easily be programmed in any language.

Build, test, and tune financial, insurance or other market trading systems using C++ algorithms and statistics. You've had an idea and have done some preliminary experiments, and it looks promising. Where do you go from here? Well, this book discusses and dissects this case study approach. Seemingly good backtest performance isn't enough to justify trading real money. You need to perform rigorous statistical tests of the system's validity. Then, if basic tests confirm the quality of your idea, you need to tune your system, not just for best performance, but also for robust behavior in the face of inevitable market changes. Next, you need to quantify its expected future behavior, assessing how bad its real-life performance might actually be, and whether you can live with that. Finally, you need to find its theoretical performance limits so you know if its actual trades conform to this theoretical expectation, enabling you to dump the system if it does not liveup to expectations.This book does not contain any sure-fire, guaranteed-riches trading systems. Those are a dime a dozen... But if you have a trading system, this book will provide you with a set of tools that will help you evaluate the potential value of your system, tweak it to improve its profitability, and monitor its on-going performance to detect deterioration before it fails catastrophically. Any serious market trader would do well to employ the methods described in this book.What You Will LearnSee how the'spaghetti-on-the-wall'approach to trading system development can be done legitimatelyDetect overfitting early in developmentEstimate the probability that your system's backtest results could have been due to just good luckRegularize a predictive model so it automatically selects an optimal subset of indicator candidatesRapidly find the global optimum for any type of parameterized trading systemAssess the ruggedness of your trading system against market changesEnhance the stationarity and information content of your proprietary indicatorsNest one layer of walkforward analysis inside another layer to account for selection bias in complex trading systemsCompute a lower bound on your system's mean future performanceBound expected periodic returns to detect on-going system deterioration before it becomes severeEstimate the probability of catastrophic drawdown Who This Book Is For Experienced C++ programmers, developers, and software engineers. Prior experience with rigorous statistical procedures to evaluate and maximize the quality of systems is recommended as well.

Improve your coding skills and learn how to write readable code. Rather than teach basic programming, this book presumes that readers understand the fundamentals, and offers time-honed best practices for style, design, documenting, testing, refactoring, and more. Taking an informal, conversational tone, author Michael Stueben offers programming stories, anecdotes, observations, advice, tricks, examples, and challenges based on his 38 years experience writing code and teaching programming classes. Trying to teach style to beginners is notoriously difficult and can easily appear pedantic. Instead, this book offers solutions and many examples to back up his ideas. Good Habits for Great Coding distills Stueben's three decades of analyzing his own mistakes, analyzing student mistakes, searching for problems that teach lessons, and searching for simple examples to illustrate complex ideas. Having found that most learn by trying out challenging problems, and reflecting on them, each chapter includes quizzes and problems. The final chapter introduces dynamic programming to reduce complex problems to subcases, and illustrates many concepts discussed in the book. Code samples are provided in Python and designed to be understandable by readers familiar with any modern programming language. At the end of this book, you will have acquired a lifetime of good coding advice, the lessons the author wishes he had learned when he was a novice.What You'll LearnCreate readable code through examples of good and bad styleWrite difficult algorithms by comparing your code to the author's codeDerive and code difficult algorithms using dynamic programmingUnderstand the psychology of the coding processWho This Book Is ForStudents or novice programmers who have taken a beginning programming course and understand coding basics.Teachers will appreciate the author's road-tested ideas that they may apply to their own teaching.

Learn about the emergence and evolution of IT in the enterprise, see how machine learning is transforming business intelligence, and discover various cognitive artificial intelligence solutions that complement and extend machine learning. In this book, author Rohit Kumar explores the challenges when these concepts intersect in IT systems by presenting detailed descriptions and business scenarios. He starts with the basics of how artificial intelligence started and how cognitive computing developed out of it. He'll explain every aspect of machine learning in detail, the reasons for changing business models to adopt it, and why your business needs it.Along the way you'll become comfortable with the intricacies of natural language processing, predictive analytics, and cognitive computing. Each technique is covered in detail so you can confidently integrate it into your enterprise as it is needed. This practical guide gives you a roadmap for transforming your business with cognitive computing, giving you the ability to work confidently in an ever-changing enterprise environment. What You'll Learn See the history of AI and how machine learning and cognitive computing evolvedDiscover why cognitive computing is so important and why your business needs itMaster the details of modern AI as it applies to enterprisesMap the path ahead in terms of your IT-business integrationAvoid common road blocks in the process of adopting cognitive computing in your business Who This Book Is For Business managers and leadership teams.

Take a deep dive into the concepts of machine learning as they apply to contemporary business and management. You will learn how machine learning techniques are used to solve fundamental and complex problems in society and industry. Machine Learning for Decision Makers serves as an excellent resource for establishing the relationship of machine learning with IoT, big data, and cognitive and cloud computing to give you an overview of how these modern areas of computing relate to each other. This book introduces a collection of the most important concepts of machine learning and sets them in context with other vital technologies that decision makers need to know about. These concepts span the process from envisioning the problem to applying machine-learning techniques to your particular situation. This discussion also provides an insight to help deploy the results to improve decision-making.The book usescase studies and jargon busting to help you grasp the theory of machine learning quickly. You'll soon gain the big picture of machine learning and how it fits with other cutting-edge IT services. This knowledge will give you confidence in your decisions for the future of your business.What You Will LearnDiscover the machine learning, big data, and cloud and cognitive computing technology stackGain insights into machine learning concepts and practices Understand business and enterprise decision-making using machine learningAbsorb machine-learning best practicesWho This Book Is ForManagers tasked with making key decisions who want to learn how and when machine learning and related technologies can help them.