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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

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.

'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.

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.

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.