The book teaches a range of design and analysis techniques for problems that arise in computing applications. The text encourages an understanding of the algorithm design process and an appreciation of the role of algorithms in the broader field of computer science.

    •Practice exercises are given at the end of chapter included conceptual questions & multiple choice questions with hints, answer & solutions. •Multiple choice questions with single correct and more the one correct answers, Comprehension based questions, Assertion-Reason, Matching Type questions, etc. •Every reaction is explained mechanistically for subject understanding. •Solutions are given at the end of every chapter for clear the concepts.

    It's as if we think of code as a magic spell, an incantation to reveal what we need to know and even what we want. Humans have always believed that certain invocations—the marriage vow, the shaman's curse—do not merely describe the world but make it. Computation casts a cultural shadow that is shaped by this long tradition of magical thinking. In this book, Ed Finn considers how the algorithm—in practical terms, “a method for solving a problem”—has its roots not only in mathematical logic but also in cybernetics, philosophy, and magical thinking.Finn argues that the algorithm deploys concepts from the idealized space of computation in a messy reality, with unpredictable and sometimes fascinating results. Drawing on sources that range from Neal Stephenson's Snow Crash to Diderot's Encyclopédie, from Adam Smith to the Star Trek computer, Finn explores the gap between theoretical ideas and pragmatic instructions. He examines the development of intelligent assistants like Siri, the rise of algorithmic aesthetics at Netflix, Ian Bogost's satiric Facebook game Cow Clicker, and the revolutionary economics of Bitcoin. He describes Google's goal of anticipating our questions, Uber's cartoon maps and black box accounting, and what Facebook tells us about programmable value, among other things.If we want to understand the gap between abstraction and messy reality, Finn argues, we need to build a model of “algorithmic reading” and scholarship that attends to process, spearheading a new experimental humanities.

     This is not your typical tinnitus book offering some “miracle cure”. It’s about changing your emotional, physical, and psychological response to the sound, with actionable techniques and specific exercises, so you can finally start to tune it out. It’s about tracking your diet, lifestyle, environment, and health to identify exactly what causes your tinnitus to spike. It’s about improving your overall health, getting better sleep, and reducing the massive amounts of stress and anxiety that tinnitus sufferers deal with on a daily basis. Too many people have been told they just have to "live with it." Too many people have been let down by emotionless doctors and "conventional" or "false" treatments. Too many people have suffered for far too long. It’s time for a change. It's time you found relief. Glenn Schweitzer was 24 years old when a rare, incurable inner ear disorder caused him to develop severe tinnitus. It disrupted nearly every aspect of his life. But today, his tinnitus no longer bothers him at all. Completely by accident, he stumbled on to simple techniques that radically rewired his mental, emotional, and physiological response to the sound.  Through Glenn’s terrifying, yet inspiring story, and with dozens of actionable techniques and tools, you can finally find the relief you deserve, too. You will learn specific techniques to reduce your tinnitus, as well as concrete steps to dramatically improve your quality of life. It may not go away entirely, but it can stop bothering you.  There isn’t a cure for tinnitus, but there is a way forward. You can live in harmony with the sound.

     But most people don't know how to harness it for profits. It took me over a decade to figure it out, and now I'm ready to share everything that I've learned. This is exactly the book that I wish I'd had when I was first learning how to trade. Don't be the sucker that Wall Street leaves holding the bag. In this book, you will learn: The one thing you must never do if a stock gaps to new highs The simplest ways to make money in the stock market How to tell when you are in a bull market, or a bear market How to identify which stocks are "market leaders" 10 ways to develop a winning trader's mindset The secrets to trading in a bear market How to use the RSI and Stochastics in different market environments How to run your trading like a business And much, much more! Join the thousands of smart traders who have improved their trading by reading this book. Amazon best-selling author and retired hedge fund manager, Matthew Kratter will teach you the secrets that he has used to trade profitably for the last 20 years. And if you ever get stuck, you can always reach out to him by email (provided inside of the book), and he will help you. Scroll to the top of this page and click BUY NOW.

    Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning.The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models.Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.

    When you direct more attention online to your brand or business, you drive more long-term revenue. Regardless of who you are or how small your business is, you can have a huge impact using free Internet tools...provided you understand and correctly apply the latest techniques.Attention! gives you an educational and motivational guide to using social media to market your brand or business online. In three parts, you'll discover everything you need to know to get off the ground and thrive in the social mediasphere, includingThe tools, techniques and tricks to get attention online and turn that attention into profit The theory behind the importance of making your mark on the Internet How other businesses and individuals made money from online marketing Whether you're just starting your business, just moving it online, or already established and looking to take your business to the next level, Attention! is the key to success.

    - Book Review Editor of the journal "Modern Pathology," the official journal of the United States-Canadian Academy of Pathology and prestigious best-selling author. This is the 5th edition of a book that has already established itself as the classic pathology textbook in India. This new edition has been updated, and improved to meet the highest standards of quality and information now required by pathology courses around the world. Editorially this new edition carries particular emphasis on molecular pathology and genetics in the pathogenesis of various diseases, and the pathological discussions of each organ or system is preceded with a short description of its structure and function. The material is integrated with extensive page cross references between chapters and the whole book has been thoroughly re-edited, with new images, illustrations and line drawings. The book is accompanied by the free student revision aid "Pathology - Quick Review and MCQs" and therefore, together as a package, "Textbook of Pathology, 5E" will be a major contribution to the required reading of undergraduate medical students worldwide.

    Whether you’re an intermediate-level Python programmer or a student of computational modeling, you’ll delve into examples of complex systems through a series of exercises, case studies, and easy-to-understand explanations.You’ll work with graphs, algorithm analysis, scale-free networks, and cellular automata, using advanced features that make Python such a powerful language. Ideal as a text for courses on Python programming and algorithms, Think Complexity will also help self-learners gain valuable experience with topics and ideas they might not encounter otherwise.Work with NumPy arrays and SciPy methods, basic signal processing and Fast Fourier Transform, and hash tablesStudy abstract models of complex physical systems, including power laws, fractals and pink noise, and Turing machinesGet starter code and solutions to help you re-implement and extend original experiments in complexityExplore the philosophy of science, including the nature of scientific laws, theory choice, realism and instrumentalism, and other topicsExamine case studies of complex systems submitted by students and readers

    

    Finding patterns and making meaningful connections inside complex data networks has emerged as one of the biggest challenges of the twenty-first century. In recent years, designers, researchers, and scientists have begun employing an innovative mix of colors, symbols, graphics, algorithms, and interactivity to clarify, and often beautify, the clutter. From representing networks of friends on Facebook to depicting interactions among proteins in a human cell, Visual Complexity presents one hundred of the most interesting examples of information-visualization by the field's leading practitioners.

    It is also about Python. However, there is much more. The study of algorithms and data structures is central to understanding what computer science is all about. Learning computer science is not unlike learning any other type of difficult subject matter. The only way to be successful is through deliberate and incremental exposure to the fundamental ideas. A beginning computer scientist needs practice so that there is a thorough understanding before continuing on to the more complex parts of the curriculum. In addition, a beginner needs to be given the opportunity to be successful and gain confidence. This textbook is designed to serve as a text for a first course on data structures and algorithms, typically taught as the second course in the computer science curriculum. Even though the second course is considered more advanced than the first course, this book assumes you are beginners at this level. You may still be struggling with some of the basic ideas and skills from a first computer science course and yet be ready to further explore the discipline and continue to practice problem solving. We cover abstract data types and data structures, writing algorithms, and solving problems. We look at a number of data structures and solve classic problems that arise. The tools and techniques that you learn here will be applied over and over as you continue your study of computer science.

    Its techniques are widely applied in engineering, science, finance, and commerce. This book is designed for a short course on machine learning. It is a short course, not a hurried course. From over a decade of teaching this material, we have distilled what we believe to be the core topics that every student of the subject should know. We chose the title `learning from data' that faithfully describes what the subject is about, and made it a point to cover the topics in a story-like fashion. Our hope is that the reader can learn all the fundamentals of the subject by reading the book cover to cover. ---- Learning from data has distinct theoretical and practical tracks. In this book, we balance the theoretical and the practical, the mathematical and the heuristic. Our criterion for inclusion is relevance. Theory that establishes the conceptual framework for learning is included, and so are heuristics that impact the performance of real learning systems. ---- Learning from data is a very dynamic field. Some of the hot techniques and theories at times become just fads, and others gain traction and become part of the field. What we have emphasized in this book are the necessary fundamentals that give any student of learning from data a solid foundation, and enable him or her to venture out and explore further techniques and theories, or perhaps to contribute their own. ---- The authors are professors at California Institute of Technology (Caltech), Rensselaer Polytechnic Institute (RPI), and National Taiwan University (NTU), where this book is the main text for their popular courses on machine learning. The authors also consult extensively with financial and commercial companies on machine learning applications, and have led winning teams in machine learning competitions.

    Is your company ready?If you read nothing else on how intelligent machines are revolutionizing business, read these 10 articles. We've combed through hundreds of Harvard Business Review articles and selected the most important ones to help you understand how these technologies work together, how to adopt them, and why your strategy can't ignore them. In this book you'll learn how: Data science, driven by artificial intelligence and machine learning, is yielding unprecedented business insights Blockchain has the potential to restructure the economy Drones and driverless vehicles are becoming essential tools 3-D printing is making new business models possible Augmented reality is transforming retail and manufacturing Smart speakers are redefining the rules of marketing Humans and machines are working together to reach new levels of productivity This collection of articles includes "Artificial Intelligence for the Real World," by Thomas H. Davenport and Rajeev Ronanki; "Stitch Fix's CEO on Selling Personal Style to the Mass Market," by Katrina Lake; "Algorithms Need Managers, Too," by Michael Luca, Jon Kleinberg, and Sendhil Mullainathan; "Marketing in the Age of Alexa," by Niraj Dawar; "Why Every Organization Needs an Augmented Reality Strategy," by Michael E. Porter and James E. Heppelmann; "Drones Go to Work," by Chris Anderson; "The Truth About Blockchain," by Marco Iansiti and Karim R. Lakhani; "The 3-D Printing Playbook," by Richard A. D’Aveni; "Collaborative Intelligence: Humans and AI Are Joining Forces," by H. James Wilson and Paul R. Daugherty; "When Your Boss Wears Metal Pants," by Walter Frick; and "Managing Our Hub Economy," by Marco Iansiti and Karim R. Lakhani.

    For years, experts have argued over the tiny details of exactly how they do what they do, and how their various components interact. What's undeniable is that, far more than being just a loudness booster the unique combination of tubes, capacitors, resistors, and transformers in these amps can contribute enormously to the quality of sound derived from any electric guitar. In this thorough and authoritative book, Dave Hunter cuts through the marketing hyperbole, and the blind faith, and supplies all the information you need to choose the right amp, and get the best from it. The book also features exclusively conducted, in-depth interviews with leading figures in the tube amp-building world - including Ken Fischer, Mark Sampson, and Michael Zaite - and even provides full instructions on how to construct your own high-quality tube guitar amp from scratch.