It also covers MySQL, and PostgreSQL. It contains examples which have been tested against each SQL platform, with incompatibilities or platform distinctives called out and explained.

    This Self-Paced Training Kit is designed to help maximize your performance on 70-680, the required exam for the Microsoft Certified Technology Specialist (MCTS): Windows 7, Configuration certification.This 2-in-1 kit includes the official Microsoft study guide, plus practice tests on CD to help you assess your skills. It comes packed with the tools and features exam candidates want most—including in-depth, self-paced training based on final exam content; rigorous, objective-by-objective review; exam tips from expert, exam-certified authors; and customizable testing options. It also provides real-world scenarios, case study examples, and troubleshooting labs to give you the skills and expertise you can use on the job.Work at your own pace through the lessons and lab exercises. This official study guide covers installing, upgrading, and migrating to Windows 7; configuring network connectivity, applications, and devices; implementing backup and recovery; configuring User Account Control (UAC), mobility options, and new features such as DirectAccess and BranchCache; and managing system updates.Then assess yourself using the 200 practice questions on CD, featuring multiple customizable testing options to meet your specific needs. Choose timed or untimed testing mode, generate random tests, or focus on discrete objectives. You get detailed explanations for right and wrong answers—including pointers back to the book for further study. You also get an exam discount voucher—making this kit an exceptional value and a great career investment.

    Deep learning networks can play poker better than professional poker players and defeat a world champion at Go. In this book, Terry Sejnowski explains how deep learning went from being an arcane academic field to a disruptive technology in the information economy.Sejnowski played an important role in the founding of deep learning, as one of a small group of researchers in the 1980s who challenged the prevailing logic-and-symbol based version of AI. The new version of AI Sejnowski and others developed, which became deep learning, is fueled instead by data. Deep networks learn from data in the same way that babies experience the world, starting with fresh eyes and gradually acquiring the skills needed to navigate novel environments. Learning algorithms extract information from raw data; information can be used to create knowledge; knowledge underlies understanding; understanding leads to wisdom. Someday a driverless car will know the road better than you do and drive with more skill; a deep learning network will diagnose your illness; a personal cognitive assistant will augment your puny human brain. It took nature many millions of years to evolve human intelligence; AI is on a trajectory measured in decades. Sejnowski prepares us for a deep learning future.

    Presenting 50 case studies of actual situations, this book demonstrates practical applications to issues faced by businesses around the globe. The rapidly evolving field of artificial intelligence has expanded beyond research labs and computer science departments and made its way into the mainstream business environment. Artificial intelligence and machine learning are cited as the most important modern business trends to drive success. It is used in areas ranging from banking and finance to social media and marketing. This technology continues to provide innovative solutions to businesses of all sizes, sectors and industries. This engaging and topical book explores a wide range of cases illustrating how businesses use AI to boost performance, drive efficiency, analyse market preferences and many others. Best-selling author and renowned AI expert Bernard Marr reveals how machine learning technology is transforming the way companies conduct business. This detailed examination provides an overview of each company, describes the specific problem and explains how AI facilitates resolution. Each case study provides a comprehensive overview, including some technical details as well as key learning summaries: Understand how specific business problems are addressed by innovative machine learning methods Explore how current artificial intelligence applications improve performance and increase efficiency in various situations Expand your knowledge of recent AI advancements in technology Gain insight on the future of AI and its increasing role in business and industry Artificial Intelligence in Practice: How 50 Successful Companies Used Artificial Intelligence to Solve Problems is an insightful and informative exploration of the transformative power of technology in 21st century commerce.

    Using sample charts, graphics, case studies, and war stories, Yankee Group Security Expert Andrew Jaquith demonstrates exactly how to establish effective metrics based on your organization's unique requirements. You'll discover how to quantify hard-to-measure security activities, compile and analyze all relevant data, identify strengths and weaknesses, set cost-effective priorities for improvement, and craft compelling messages for senior management. Security Metrics successfully bridges management's quantitative viewpoint with the nuts-and-bolts approach typically taken by security professionals. It brings together expert solutions drawn from Jaquith's extensive consulting work in the software, aerospace, and financial services industries, including new metrics presented nowhere else. You'll learn how to: - Replace nonstop crisis response with a systematic approach to security improvement - Understand the differences between "good" and "bad" metrics - Measure coverage and control, vulnerability management, password quality, patch latency, benchmark scoring, and business-adjusted risk - Quantify the effectiveness of security acquisition, implementation, and other program activities - Organize, aggregate, and analyze your data to bring out key insights - Use visualization to understand and communicate security issues more clearly - Capture valuable data from firewalls and antivirus logs, third-party auditor reports, and other resources - Implement balanced scorecards that present compact, holistic views of organizational security effectiveness Whether you're an engineer or consultant responsible for security and reporting to management-or an executive who needs better information for decision-making-Security Metrics is the resource you have been searching for. Andrew Jaquith, program manager for Yankee Group's Security Solutions and Services Decision Service, advises enterprise clients on prioritizing and managing security resources. He also helps security vendors develop product, service, and go-to-market strategies for reaching enterprise customers. He co-founded @stake, Inc., a security consulting pioneer acquired by Symantec Corporation in 2004. His application security and metrics research has been featured in CIO, CSO, InformationWeek, IEEE Security and Privacy, and The Economist. Foreword Preface Acknowledgments About the Author Chapter 1 Introduction: Escaping the Hamster Wheel of Pain Chapter 2 Defining Security Metrics Chapter 3 Diagnosing Problems and Measuring Technical Security Chapter 4 Measuring Program Effectiveness Chapter 5 Analysis Techniques Chapter 6 Visualization Chapter 7 Automating Metrics Calculations Chapter 8 Designing Security Scorecards Index

    

    By reconstructing authentic, complex AI programs using state-of-the-art Common Lisp, the book teaches students and professionals how to build and debug robust practical programs, while demonstrating superior programming style and important AI concepts. The author strongly emphasizes the practical performance issues involved in writing real working programs of significant size. Chapters on troubleshooting and efficiency are included, along with a discussion of the fundamentals of object-oriented programming and a description of the main CLOS functions. This volume is an excellent text for a course on AI programming, a useful supplement for general AI courses and an indispensable reference for the professional programmer.

    Each chapter is relatively self-contained and can be used as a unit of study. The algorithms are described in English and in a pseudocode designed to be readable by anyone who has done a little programming. The explanations have been kept elementary without sacrificing depth of coverage or mathematical rigor.

    However, despite all of the recent advances in computer vision research, the dream of having a computer interpret an image at the same level as a two-year old remains elusive. Why is computer vision such a challenging problem and what is the current state of the art?Computer Vision: Algorithms and Applications explores the variety of techniques commonly used to analyze and interpret images. It also describes challenging real-world applications where vision is being successfully used, both for specialized applications such as medical imaging, and for fun, consumer-level tasks such as image editing and stitching, which students can apply to their own personal photos and videos.More than just a source of "recipes," this exceptionally authoritative and comprehensive textbook/reference also takes a scientific approach to basic vision problems, formulating physical models of the imaging process before inverting them to produce descriptions of a scene. These problems are also analyzed using statistical models and solved using rigorous engineering techniquesTopics and features: Structured to support active curricula and project-oriented courses, with tips in the Introduction for using the book in a variety of customized courses Presents exercises at the end of each chapter with a heavy emphasis on testing algorithms and containing numerous suggestions for small mid-term projects Provides additional material and more detailed mathematical topics in the Appendices, which cover linear algebra, numerical techniques, and Bayesian estimation theory Suggests additional reading at the end of each chapter, including the latest research in each sub-field, in addition to a full Bibliography at the end of the book Supplies supplementary course material for students at the associated website, http: //szeliski.org/Book/ Suitable for an upper-level undergraduate or graduate-level course in computer science or engineering, this textbook focuses on basic techniques that work under real-world conditions and encourages students to push their creative boundaries. Its design and exposition also make it eminently suitable as a unique reference to the fundamental techniques and current research literature in computer vision.

    The R language provides everything you need to do statistical work, but its structure can be difficult to master. This collection of concise, task-oriented recipes makes you productive with R immediately, with solutions ranging from basic tasks to input and output, general statistics, graphics, and linear regression.Each recipe addresses a specific problem, with a discussion that explains the solution and offers insight into how it works. If you're a beginner, R Cookbook will help get you started. If you're an experienced data programmer, it will jog your memory and expand your horizons. You'll get the job done faster and learn more about R in the process.Create vectors, handle variables, and perform other basic functionsInput and output dataTackle data structures such as matrices, lists, factors, and data framesWork with probability, probability distributions, and random variablesCalculate statistics and confidence intervals, and perform statistical testsCreate a variety of graphic displaysBuild statistical models with linear regressions and analysis of variance (ANOVA)Explore advanced statistical techniques, such as finding clusters in your dataWonderfully readable, R Cookbook serves not only as a solutions manual of sorts, but as a truly enjoyable way to explore the R language--one practical example at a time.--Jeffrey Ryan, software consultant and R package author

    Want to impress your coworkers and write the fastest, most efficient, stable code you ever have? Don't reinvent the wheel. Reuse decades of research into battle-tested, highly optimized, and proven techniques available on any Unix system.This book will teach you what you need to know so that you can write your own servers, debug your entire stack when things go awry, and understand how things are working under the hood.http://www.jstorimer.com/products/wor...

    In today’s economy, we have far too much dismay along with our amazement, and technology bears some of the blame. In this combination of memoir, business strategy guide, and call to action, Tim O'Reilly, Silicon Valley’s leading intellectual and the founder of O’Reilly Media, explores the upside and the potential downsides of today's WTF? technologies. What is the future when an increasing number of jobs can be performed by intelligent machines instead of people, or done only by people in partnership with those machines? What happens to our consumer based societies—to workers and to the companies that depend on their purchasing power? Is income inequality and unemployment an inevitable consequence of technological advancement, or are there paths to a better future? What will happen to business when technology-enabled networks and marketplaces are better at deploying talent than traditional companies? How should companies organize themselves to take advantage of these new tools? What’s the future of education when on-demand learning outperforms traditional institutions? How can individuals continue to adapt and retrain? Will the fundamental social safety nets of the developed world survive the transition, and if not, what will replace them? O'Reilly is "the man who can really can make a whole industry happen," according to Eric Schmidt, Executive Chairman of Alphabet (Google.) His genius over the past four decades has been to identify and to help shape our response to emerging technologies with world shaking potential—the World Wide Web, Open Source Software, Web 2.0, Open Government data, the Maker Movement, Big Data, and now AI. O’Reilly shares the techniques he's used at O’Reilly Media  to make sense of and predict past innovation waves and applies those same techniques to provide a framework for thinking about how today’s world-spanning platforms and networks, on-demand services, and artificial intelligence are changing the nature of business, education, government, financial markets, and the economy as a whole. He provides tools for understanding how all the parts of modern digital businesses work together to create marketplace advantage and customer value, and why ultimately, they cannot succeed unless their ecosystem succeeds along with them.The core of the book's call to action is an exhortation to businesses to DO MORE with technology rather than just using it to cut costs and enrich their shareholders. Robots are going to take our jobs, they say. O'Reilly replies, “Only if that’s what we ask them to do! Technology is the solution to human problems, and we won’t run out of work till we run out of problems." Entrepreneurs need to set their sights on how they can use big data, sensors, and AI to create amazing human experiences and the economy of the future, making us all richer in the same way the tools of the first industrial revolution did. Yes, technology can eliminate labor and make things cheaper, but at its best, we use it to do things that were previously unimaginable! What is our poverty of imagination? What are the entrepreneurial leaps that will allow us to use the technology of today to build a better future, not just a more efficient one? Whether technology brings the WTF? of wonder or the WTF? of dismay isn't inevitable. It's up to us!

    Ideal for processing large datasets, the Apache Hadoop framework is an open source implementation of the MapReduce algorithm on which Google built its empire. This comprehensive resource demonstrates how to use Hadoop to build reliable, scalable, distributed systems: programmers will find details for analyzing large datasets, and administrators will learn how to set up and run Hadoop clusters. Complete with case studies that illustrate how Hadoop solves specific problems, this book helps you:Use the Hadoop Distributed File System (HDFS) for storing large datasets, and run distributed computations over those datasets using MapReduce Become familiar with Hadoop's data and I/O building blocks for compression, data integrity, serialization, and persistence Discover common pitfalls and advanced features for writing real-world MapReduce programs Design, build, and administer a dedicated Hadoop cluster, or run Hadoop in the cloud Use Pig, a high-level query language for large-scale data processing Take advantage of HBase, Hadoop's database for structured and semi-structured data Learn ZooKeeper, a toolkit of coordination primitives for building distributed systems If you have lots of data -- whether it's gigabytes or petabytes -- Hadoop is the perfect solution. Hadoop: The Definitive Guide is the most thorough book available on the subject. "Now you have the opportunity to learn about Hadoop from a master-not only of the technology, but also of common sense and plain talk." -- Doug Cutting, Hadoop Founder, Yahoo!

    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.

    With this practical book, you’ll learn how to design and implement a graph database that brings the power of graphs to bear on a broad range of problem domains. Whether you want to speed up your response to user queries or build a database that can adapt as your business evolves, this book shows you how to apply the schema-free graph model to real-world problems.Learn how different organizations are using graph databases to outperform their competitors. With this book’s data modeling, query, and code examples, you’ll quickly be able to implement your own solution.Model data with the Cypher query language and property graph modelLearn best practices and common pitfalls when modeling with graphsPlan and implement a graph database solution in test-driven fashionExplore real-world examples to learn how and why organizations use a graph databaseUnderstand common patterns and components of graph database architectureUse analytical techniques and algorithms to mine graph database information