For eight editions, it has been known for its thorough coverage, student-friendly tone, model interior design, and abundant samples of the techniques and guidelines discussed throughout the book. As always, Mike Markel keeps pace with current technologies and the realities of technical communication today.

    Contents include: 'Personality Theory in Context', 'Cognitive Personality Theories', 'An Introduction to Intelligence', 'The Application of Personality and Intelligence in Education and the Workplace', 'Optimism', 'Interpersonal Relationships' and 'Psychometric Testing'.

    The changing nature of consumer expectations means that marketers must learn how to build communities in addition to brand loyalty. With its interactive design and in-depth, real-world examples and cases, the South Asian edition of Principles of Marketing helps students learn how to create customer value, target the correct market, and build customer relationships.

    Therefore, this book provides the fundamental concepts and techniques of real analysis for readers in all of these areas. It helps one develop the ability to think deductively, analyze mathematical situations and extend ideas to a new context. Like the first two editions, this edition maintains the same spirit and user-friendly approach with some streamlined arguments, a few new examples, rearranged topics, and a new chapter on the Generalized Riemann Integral.

    Boylestad and Nashelsky offer students a complete and comprehensive survey, focusing on all the essentials they will need to succeed on the job. This very readable presentation is supported by strong pedagogy and content that is ideal for new students of this rapidly changing field. Its colorful, student-friendly layout boasts a large number of stunning photographs. A broad range of ancillary materials is available for instructor support.

    So while the focus is on design, the approach is holistic. Hence, the book speaks to designers, usability specialists, the HCI community, product managers, and business executives. There is an emphasis on balancing the back-end concern with usability and engineering excellence (getting the design right) with an up-front investment in sketching and ideation (getting the right design). Overall, the objective is to build the notion of informed design: molding emerging technology into a form that serves our society and reflects its values.Grounded in both practice and scientific research, Bill Buxton s engaging work aims to spark the imagination while encouraging the use of new techniques, breathing new life into user experience design. Covers sketching and early prototyping design methods suitable for dynamic product capabilities: cell phones that communicate with each other and other embedded systems, "smart" appliances, and things you only imagine in your dreamsThorough coverage of the design sketching method which helps easily build experience prototypes-without the effort of engineering prototypes which are difficult to abandonReaches out to a range of designers, including user interface designers, industrial designers, software engineers, usability engineers, product managers, and othersFull of case studies, examples, exercises, and projects, and access to video clips that demonstrate the principles and methods"

    Readers learn how to reduce time constraints and develop programs efficiently by analyzing the feasibility of an algorithm before it is coded. The C++ language is brought up-to-date and simplified, and the Standard Template Library is now fully incorporated throughout the text. This Third Edition also features significantly revised coverage of lists, stacks, queues, and trees and an entire chapter dedicated to amortized analysis and advanced data structures such as the Fibonacci heap. Known for its clear and friendly writing style, Data Structures and Algorithm Analysis in C++ is logically organized to cover advanced data structures topics from binary heaps to sorting to NP-completeness. Figures and examples illustrating successive stages of algorithms contribute to Weiss' careful, rigorous and in-depth analysis of each type of algorithm.

    I. is explored and explained in this best selling text. Assuming no prior knowledge, it covers topics like neural networks and robotics. This text explores the range of problems which have been and remain to be solved using A. I. tools and techniques. The second half of this text is an excellent reference.

    But how can you get started working in a wide-ranging, interdisciplinary field that’s so clouded in hype? This insightful book, based on Columbia University’s Introduction to Data Science class, tells you what you need to know.In many of these chapter-long lectures, data scientists from companies such as Google, Microsoft, and eBay share new algorithms, methods, and models by presenting case studies and the code they use. If you’re familiar with linear algebra, probability, and statistics, and have programming experience, this book is an ideal introduction to data science.Topics include:Statistical inference, exploratory data analysis, and the data science processAlgorithmsSpam filters, Naive Bayes, and data wranglingLogistic regressionFinancial modelingRecommendation engines and causalityData visualizationSocial networks and data journalismData engineering, MapReduce, Pregel, and HadoopDoing Data Science is collaboration between course instructor Rachel Schutt, Senior VP of Data Science at News Corp, and data science consultant Cathy O’Neil, a senior data scientist at Johnson Research Labs, who attended and blogged about the course.

    This guide also helps you understand the many data-mining techniques in use today.Based on an MBA course Provost has taught at New York University over the past ten years, Data Science for Business provides examples of real-world business problems to illustrate these principles. You’ll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your company’s data science projects. You’ll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making.Understand how data science fits in your organization—and how you can use it for competitive advantageTreat data as a business asset that requires careful investment if you’re to gain real valueApproach business problems data-analytically, using the data-mining process to gather good data in the most appropriate wayLearn general concepts for actually extracting knowledge from dataApply data science principles when interviewing data science job candidates

    This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree- based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.

    Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, recognize faces or spoken speech, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data. "Introduction to Machine Learning" is a comprehensive textbook on the subject, covering a broad array of topics not usually included in introductory machine learning texts. It discusses many methods based in different fields, including statistics, pattern recognition, neural networks, artificial intelligence, signal processing, control, and data mining, in order to present a unified treatment of machine learning problems and solutions. All learning algorithms are explained so that the student can easily move from the equations in the book to a computer program. The book can be used by advanced undergraduates and graduate students who have completed courses in computer programming, probability, calculus, and linear algebra. It will also be of interest to engineers in the field who are concerned with the application of machine learning methods.After an introduction that defines machine learning and gives examples of machine learning applications, the book covers supervised learning, Bayesian decision theory, parametric methods, multivariate methods, dimensionality reduction, clustering, nonparametric methods, decision trees, linear discrimination, multilayer perceptrons, local models, hidden Markov models, assessing and comparing classification algorithms, combining multiple learners, and reinforcement learning.

    This guide, which can be used with any text or can stand alone, contains at the beginning of each chapter a list of key definitions, a summary of major concepts, step by step solutions to dozens of problems, and additional practice problems.

    We start small and then build on what you learn to scale out to multi-site, multi-tier applications. The book is written for both developers and sysadmins. We focus on building monitored and measurable applications. We also use tools that are designed to handle the challenges of managing Cloud, containerised and distributed applications and infrastructure.In the book we'll deliver:* An introduction to monitoring, metrics and measurement.* A scalable framework for monitoring hosts (including Docker and containers), services and applications built on top of the Riemann event stream processor. * Graphing and metric storage using Graphite and Grafana.* Logging with Logstash.* A framework for high quality and useful notifications* Techniques for developing and building monitorable applications* A capstone that puts all the pieces together to monitor a multi-tier application.

    Using the concise, scenario-driven style pioneered in Scott Meyers' best-selling Effective C++, Matt Galloway brings together 52 Objective-C best practices, tips, shortcuts, and realistic code examples that are available nowhere else. Through real-world examples, Galloway uncovers little-known Objective-C quirks, pitfalls, and intricacies that powerfully impact code behavior and performance. You'll learn how to choose the most efficient and effective way to accomplish key tasks when multiple options exist, and how to write code that's easier to understand, maintain, and improve. Galloway goes far beyond the core language, helping you integrate and leverage key Foundation framework classes and modern system libraries, such as Grand Central Dispatch. Coverage includes Optimizing interactions and relationships between Objective-C objects Mastering interface and API design: writing classes that feel "right at home" Using protocols and categories to write maintainable, bug-resistant code Avoiding memory leaks that can still occur even with Automatic Reference Counting (ARC) Writing modular, powerful code with Blocks and Grand Central Dispatch Leveraging differences between Objective-C protocols and multiple inheritance in other languages Improving code by more effectively using arrays, dictionaries, and sets Uncovering surprising power in the Cocoa and Cocoa Touch frameworks