Sousa discusses the cognitive mechanisms for learning mathematics and the environmental and developmental factors that contribute to mathematics difficulties. This award-winning text examines:Children's innate number sense and how the brain develops an understanding of number relationships Rationales for modifying lessons to meet the developmental learning stages of young children, preadolescents, and adolescents How to plan lessons in PreK-12 mathematics Implications of current research for planning mathematics lessons, including discoveries about memory systems and lesson timing Methods to help elementary and secondary school teachers detect mathematics difficulties Clear connections to the NCTM standards and curriculum focal points

    This second edition is appropriate as a supplementary (companion) text for any college-level course covering digital signal processing.

    Providing a concise introduction to abstract algebra, this work unfolds some of the fundamental systems with the aim of reaching applicable, significant results.

    Unnikrishna Pillai of Polytechnic University. The book is intended for a senior/graduate level course in probability and is aimed at students in electrical engineering, math, and physics departments. The authors' approach is to develop the subject of probability theory and stochastic processes as a deductive discipline and to illustrate the theory with basic applications of engineering interest. Approximately 1/3 of the text is new material--this material maintains the style and spirit of previous editions. In order to bridge the gap between concepts and applications, a number of additional examples have been added for further clarity, as well as several new topics.

    The framework of probabilistic graphical models, presented in this book, provides a general approach for this task. The approach is model-based, allowing interpretable models to be constructed and then manipulated by reasoning algorithms. These models can also be learned automatically from data, allowing the approach to be used in cases where manually constructing a model is difficult or even impossible. Because uncertainty is an inescapable aspect of most real-world applications, the book focuses on probabilistic models, which make the uncertainty explicit and provide models that are more faithful to reality.Probabilistic Graphical Models discusses a variety of models, spanning Bayesian networks, undirected Markov networks, discrete and continuous models, and extensions to deal with dynamical systems and relational data. For each class of models, the text describes the three fundamental cornerstones: representation, inference, and learning, presenting both basic concepts and advanced techniques. Finally, the book considers the use of the proposed framework for causal reasoning and decision making under uncertainty. The main text in each chapter provides the detailed technical development of the key ideas. Most chapters also include boxes with additional material: skill boxes, which describe techniques; case study boxes, which discuss empirical cases related to the approach described in the text, including applications in computer vision, robotics, natural language understanding, and computational biology; and concept boxes, which present significant concepts drawn from the material in the chapter. Instructors (and readers) can group chapters in various combinations, from core topics to more technically advanced material, to suit their particular needs.

    One half massive case study, one half technical manual, Dissecting the Hack has it all - learn all about hacking tools and techniques and how to defend your network against threats. Yes, the security threats are real - read more about the tactics that you see executed throughout the story in the second half of the book where you will learn to recon, scan, explore, exploit and expunge with the tools and techniques shown in the story. Every hack is real and can be used by you once you have the knowledge within this book! Utilizes actual hacking and security tools in its story- helps to familiarize a newbie with the many devices and their codeIntroduces basic hacking techniques in real life context for ease of learningPresented in the words of the hacker/security pro, effortlessly envelops the beginner in the language of the hack

    Today, unfortunately, the traditional place of mathematics in education is in grave danger. The teaching and learning of mathematics has degenerated into the realm of rote memorization, the outcome of which leads to satisfactory formal ability but does not lead to real understanding or to greater intellectual independence. This new edition of Richard Courant's and Herbert Robbins's classic work seeks to address this problem. Its goal is to put the meaning back into mathematics.Written for beginners and scholars, for students and teachers, for philosophers and engineers, What is Mathematics? Second Edition is a sparkling collection of mathematical gems that offers an entertaining and accessible portrait of the mathematical world. Covering everything from natural numbers and the number system to geometrical constructions and projective geometry, from topology and calculus to matters of principle and the Continuum Hypothesis, this fascinating survey allows readers to delve into mathematics as an organic whole rather than an empty drill in problem solving. With chapters largely independent of one another and sections that lead upward from basic to more advanced discussions, readers can easily pick and choose areas of particular interest without impairing their understanding of subsequent parts.Brought up to date with a new chapter by Ian Stewart, What is Mathematics? Second Edition offers new insights into recent mathematical developments and describes proofs of the Four-Color Theorem and Fermat's Last Theorem, problems that were still open when Courant and Robbins wrote this masterpiece, but ones that have since been solved.Formal mathematics is like spelling and grammar - a matter of the correct application of local rules. Meaningful mathematics is like journalism - it tells an interesting story. But unlike some journalism, the story has to be true. The best mathematics is like literature - it brings a story to life before your eyes and involves you in it, intellectually and emotionally. What is Mathematics is like a fine piece of literature - it opens a window onto the world of mathematics for anyone interested to view.

    But for sheer cantankerousness, fighting moxie, and will to win, very few have come close to Leo "the Lip" Durocher. Following a five decade career as a player and manager for baseball's most storied franchises, Durocher teamed up with veteran sportswriter Ed Linn to tell the story of his life in the game. The resulting book, Nice Guys Finish Last, is baseball at its best, brimming with personality and full of all the fights and feuds, triumphs and tricks that made Durocher such a success and an outsized celebrity.

    However, these activities can be viewed as two facets of the same field, and together they have undergone substantial development over the past ten years. In particular, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic models. Also, the practical applicability of Bayesian methods has been greatly enhanced through the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation. Similarly, new models based on kernels have had a significant impact on both algorithms and applications. This new textbook reflects these recent developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first-year PhD students, as well as researchers and practitioners, and assumes no previous knowledge of pattern recognition or machine learning concepts. Knowledge of multivariate calculus and basic linear algebra is required, and some familiarity with probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.

    It is unusual, remarkable, for a major writer also to be an accomplished photographer. Eudora Welty is one of the very few whose great talent has been expressed in both photographs and fiction. This book, Eudora Welty: Photographs, brings together in one volume about 250 representative photographs from the few thousand that she took during the 1930s, '40s, and '50s. Although her camera's view finder compresses much, like the frame in which she conceives her fiction, it finds elements that convey her deep compassion and her artist's sensibilities. From the confines of her native Mississippi these photographs unfold the world of Eudora Welty's art, reaching, extending, and exploring. In the Deep South of Depression times, when she began writing, she discovered the place into which she had been born and which would always be her subject. From here, as these photographs show, she approached and risked the outside world. From rural Mississippi to New Orleans, Charleston, New York City, and Yaddo, and then to Ireland, England, and the Continent, Welty widened her vision and expanded her art. These photographs reveal that both in her fiction and in the pictures she took it has always been in place, in the special qualities of what is local, that she found her impulse.

    Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications.Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications. The only necessary mathematical background is familiarity with elementary concepts of probability.The book is divided into three parts. Part I defines the reinforcement learning problem in terms of Markov decision processes. Part II provides basic solution methods: dynamic programming, Monte Carlo methods, and temporal-difference learning. Part III presents a unified view of the solution methods and incorporates artificial neural networks, eligibility traces, and planning; the two final chapters present case studies and consider the future of reinforcement learning.

    In The Music of James Bond, author Jon Burlingame throws open studio and courtroom doors alike to reveal the full and extraordinary history of the soundsof James Bond, spicing the story with a wealth of fascinating and previously undisclosed tales.Burlingame devotes a chapter to each Bond film, providing the backstory for the music (including a reader-friendly analysis of each score) from the last-minute creation of the now-famous James Bond Theme in Dr. No to John Barry's trend-setting early scores for such films as Goldfinger andThunderball. We learn how synthesizers, disco and modern electronica techniques played a role in subsequent scores, and how composer David Arnold reinvented the Bond sound for the 1990s and beyond.The book brims with behind-the-scenes anecdotes. Burlingame examines the decades-long controversy over authorship of the Bond theme; how Frank Sinatra almost sang the title song for Moonraker; and how top artists like Shirley Bassey, Tom Jones, Paul McCartney, Carly Simon, Duran Duran, GladysKnight, Tina Turner, and Madonna turned Bond songs into chart-topping hits. The author shares the untold stories of how Eric Clapton played guitar for Licence to Kill but saw his work shelved, and how Amy Winehouse very nearly co-wrote and sang the theme for Quantum of Solace.New interviews with many Bond songwriters and composers, coupled with extensive research as well as fascinating and previously undiscovered details--temperamental artists, unexpected hits, and the convergence of great music and unforgettable imagery--make The Music of James Bond a must read for 007buffs and all popular music fans. This paperback edition is brought up-to-date with a new chapter on Skyfall.

    Includes expert coverage of new topics: Turbocodes, Turboequalization, Antenna Arrays, Digital Cellular Systems, and Iterative Detection. Convenient, sequential organization begins with a look at the historyo and classification of channel models and builds from there.

    It provides a simple mathematical framework which can be used to understand and even design biological circuits. The textavoids specialist terms, focusing instead on several well-studied biological systems that concisely demonstrate key principles. An Introduction to Systems Biology: Design Principles of Biological Circuits builds a solid foundation for the intuitive understanding of general principles. It encourages the reader to ask why a system is designed in a particular way and then proceeds to answer with simplified models.

    This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You’ll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you’ve learned along the way. You’ll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true "signals" in your dataset Communicate—learn R Markdown for integrating prose, code, and results