During that week, you will learn almost everything modern machine learning has to offer. The author and other practitioners have spent years learning these concepts.Companion wiki — the book has a continuously updated wiki that extends some book chapters with additional information: Q&A, code snippets, further reading, tools, and other relevant resources.Flexible price and formats — choose from a variety of formats and price options: Kindle, hardcover, paperback, EPUB, PDF. If you buy an EPUB or a PDF, you decide the price you pay!Read first, buy later — download book chapters for free, read them and share with your friends and colleagues. Only if you liked the book or found it useful in your work, study or business, then buy it.

    It is suitable for a one or two semester university course on AI, as well as for researchers in the field.

    Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach.The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package—PMTK (probabilistic modeling toolkit)—that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.

    This introduction is ideal for beginners: it requires no previous programming experience and all concepts are explained from first principles via carefully chosen examples. Each chapter includes exercises that range from the straightforward to extended projects, plus suggestions for further reading on more advanced topics. The author is a leading Haskell researcher and instructor, well-known for his teaching skills. The presentation is clear and simple, and benefits from having been refined and class-tested over several years. The result is a text that can be used with courses, or for self-learning. Features include freely accessible Powerpoint slides for each chapter, solutions to exercises and examination questions (with solutions) available to instructors, and a downloadable code that's fully compliant with the latest Haskell release.

    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.

    What's been lacking is the expertise to fuse them into solutions to real-world problems. These patterns are the intellectual mortar for J2EE software construction." —John Vlissides, co-author of Design Patterns, the "Gang of Four" book"The authors of Core J2EE Patterns have harvested a really useful set of patterns. They show how to apply these patterns and how to refactor your system to take advantage of them. It's just like having a team of experts sitting at your side."—Grady Booch, Chief Scientist, Rational Software Corporation "The authors do a great job describing useful patterns for application architectures. The section on refactoring is worth the price of the entire book!"—Craig McClanahan, Struts Lead Architect and Specification Lead for JavaServer Faces "Core J2EE Patterns is the gospel that should accompany every J2EE application server...Built upon the in-the-trenches expertise of its veteran architect authors, this volume unites the platform's many technologies and APIs in a way that application architects can use, and provides insightful answers to the whys, whens, and hows of the J2EE platform."—Sean Neville, JRun Enterprise Architect, MacromediaDevelopers often confuse learning the technology with learning to design with the technology. In this book, senior architects from the Sun Java Center share their cumulative design experience on Java 2 Platform, Enterprise Edition (J2EE) technology.The primary focus of the book is on patterns, best practices, design strategies, and proven solutions using the key J2EE technologies including JavaServer Pages(TM) (JSP(TM)), Servlets, Enterprise JavaBeans(TM) (EJB(TM)), and Java(TM) Message Service (JMS) APIs. The J2EE Pattern Catalog with 21 patterns and numerous strategies is presented to document and promote best practices for these technologies.Core J2EE Patterns, Second Edition offers the following: J2EE Pattern Catalog with 21 patterns—fully revised and newly documented patterns providing proven solutions for enterprise applications Design strategies for the presentation tier, business tier, and integration tier Coverage of servlets, JSP, EJB, JMS, and Web Services J2EE technology bad practices Refactorings to improve existing designs using patterns Fully illustrated with UML diagrams Extensive sample code for patterns, strategies, and refactorings

    This easy-to-follow guide is illustrated with lots of examples, and leads readers through the process of creating great websites from start to finish using HTML5 and CSS3.It also features details on all the new HTML5 and CSS3 elements and features information on the current level of browser support advice for creating great experiences for all users thanks to progressive enhancement.

    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.

    With this insightful book, intermediate to experienced programmers interested in data analysis will learn techniques for working with data in a business environment. You'll learn how to look at data to discover what it contains, how to capture those ideas in conceptual models, and then feed your understanding back into the organization through business plans, metrics dashboards, and other applications.Along the way, you'll experiment with concepts through hands-on workshops at the end of each chapter. Above all, you'll learn how to think about the results you want to achieve -- rather than rely on tools to think for you.Use graphics to describe data with one, two, or dozens of variablesDevelop conceptual models using back-of-the-envelope calculations, as well asscaling and probability argumentsMine data with computationally intensive methods such as simulation and clusteringMake your conclusions understandable through reports, dashboards, and other metrics programsUnderstand financial calculations, including the time-value of moneyUse dimensionality reduction techniques or predictive analytics to conquer challenging data analysis situationsBecome familiar with different open source programming environments for data analysisFinally, a concise reference for understanding how to conquer piles of data.--Austin King, Senior Web Developer, MozillaAn indispensable text for aspiring data scientists.--Michael E. Driscoll, CEO/Founder, Dataspora

    This easy-to-read guide helps you cultivate a host of good development practices, based on classic software design patterns and new patterns unique to embedded programming. Learn how to build system architecture for processors, not operating systems, and discover specific techniques for dealing with hardware difficulties and manufacturing requirements.Written by an expert who’s created embedded systems ranging from urban surveillance and DNA scanners to children’s toys, this book is ideal for intermediate and experienced programmers, no matter what platform you use.Optimize your system to reduce cost and increase performanceDevelop an architecture that makes your software robust in resource-constrained environmentsExplore sensors, motors, and other I/O devicesDo more with less: reduce RAM consumption, code space, processor cycles, and power consumptionLearn how to update embedded code directly in the processorDiscover how to implement complex mathematics on small processorsUnderstand what interviewers look for when you apply for an embedded systems job"Making Embedded Systems is the book for a C programmer who wants to enter the fun (and lucrative) world of embedded systems. It’s very well written—entertaining, even—and filled with clear illustrations." —Jack Ganssle, author and embedded system expert.

    Full of insights, arguments and philosophical perspectives, the book covers an amazing array of topics. Beginning in antiquity with Democritus, it progresses through logic and set theory, computability and complexity theory, quantum computing, cryptography, the information content of quantum states and the interpretation of quantum mechanics. There are also extended discussions about time travel, Newcomb's Paradox, the anthropic principle and the views of Roger Penrose. Aaronson's informal style makes this fascinating book accessible to readers with scientific backgrounds, as well as students and researchers working in physics, computer science, mathematics and philosophy.

    A simple web search picks out a handful of relevant needles from the world's biggest haystack: the billions of pages on the World Wide Web. Uploading a photo to Facebook transmits millions of pieces of information over numerous error-prone network links, yet somehow a perfect copy of the photo arrives intact. Without even knowing it, we use public-key cryptography to transmit secret information like credit card numbers; and we use digital signatures to verify the identity of the websites we visit. How do our computers perform these tasks with such ease? This is the first book to answer that question in language anyone can understand, revealing the extraordinary ideas that power our PCs, laptops, and smartphones. Using vivid examples, John MacCormick explains the fundamental "tricks" behind nine types of computer algorithms, including artificial intelligence (where we learn about the "nearest neighbor trick" and "twenty questions trick"), Google's famous PageRank algorithm (which uses the "random surfer trick"), data compression, error correction, and much more. These revolutionary algorithms have changed our world: this book unlocks their secrets, and lays bare the incredible ideas that our computers use every day.

    He explains why invisible and unmanaged queues are the underlying root cause of poor product development performance. He shows why these queues form and how they undermine the speed, quality, and efficiency in product development.

    Fortunately, there s a solution: Erik T. Ray s Learning XML, Second Edition. This book presents an outstanding birds-eye view of the XML landscape. It s definitely not a programming book (though it does introduce some key XML programming issues). Rather, it s focused on key ideas you need to understand whatever you want to do with XML. That could be document management, web or print content delivery, application integration, B2B commerce, data storage, internationalization -- you name it.Ray s day job is software developer and XML specialist at O Reilly. There, he s helped to implement a complete publishing solution, using DocBook-XML and Perl to produce books in print, on CD-ROM, and for online delivery. So he understands XML from the real-world point of view of someone with a job to do. His first goal is to take on the big questions. First, What is XML? Ray attacks this question from multiple angles, introducing XML as a general-purpose information storage system, a markup language toolkit, and an open standard (or, increasingly, a collection of standards). What can (and can t) you do with XML? What s the history that led us here? And what tools do you need to get started? Next, he introduces the basic building blocks of XML markup and all XML-derived languages: stuff you ll need to know regardless of your goals. Through easy examples, you ll understand elements, attributes, entities, and processing instructions -- and how they fit together in a well-formed XML document. Then, it s on to representing information with XML -- in other words, understanding the nature and planning the structure of the documents you ll be using. Ray starts simply, then builds on his basic examples to discuss narrative documents with text flows, block and inline elements, and titled sections. Once you can handle those, he discusses more complex information modeling, as used in specialized markup languages such as VML. This edition contains an entirely new chapter on XML Schemas -- what he calls the shepherds that keep documents from straying outside of the herd and causing trouble. Schemas, of course, have become hugely important. This is one of the best plain-English introductions to the topic we ve seen. Ray then turns to presentation, introducing CSS stylesheets, basic usage, rule matching, properties, and more. A little later on, he returns to the subject -- this time with a complete introduction to XSL-FO that illuminates two powerful examples. The first is TEI-XML, a markup language for scholarly documents (Ray presents a Shakespearean sonnet, appropriately coded). The second is the immensely powerful DocBook -- which, as we ve observed, Ray knows inside and out. Learning XML is superbly written. Clear explanations. Simple examples. Great metaphors and analogies. And excellent introductions to nearly every topic that matters, from links to presentation, transformation to internationalization. If you re just starting out with XML, you re lucky to have it. Bill CamardaBill Camarda is a consultant, writer, and web/multimedia content developer. His 15 books include Special Edition Using Word 2000 and Upgrading & Fixing Networks for Dummies, Second Edition.