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

    In this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch. If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today’s messy glut of data holds answers to questions no one’s even thought to ask. This book provides you with the know-how to dig those answers out. Get a crash course in Python Learn the basics of linear algebra, statistics, and probability—and understand how and when they're used in data science Collect, explore, clean, munge, and manipulate data Dive into the fundamentals of machine learning Implement models such as k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clustering Explore recommender systems, natural language processing, network analysis, MapReduce, and databases

    A no-nonsense tutorial and reliable reference, this book features thoroughly tested real-world examples. The most important language and library features are demonstrated with deliberately simple sample programs, but they aren't fake and they don't cut corners. More importantly, all of the programs have been updated for J2SE 5.0 and should make good starting points for your own code. You won't find any toy examples here. This is a book for programmers who want to write real code to solve real problems. Cay S. Horstmann is a professor of computer science at San Jose State University. Previously he was vice president and chief technology officer of Preview Systems Inc. and a consultant on C++, Java, and Internet programming for major corporations, universities, and organizations. Gary Cornell has written or cowritten more than twenty popular computer books. He has a Ph.D. from Brown University and has been a visiting scientist at IBM Watson Laboratories, as well as a professor at the University of Connecticut.

    But many organizations adopting these technologies have found that it only leads to a faster-growing sprawl of unmanageable systems. This is where infrastructure as code can help. With this practical guide, author Kief Morris of ThoughtWorks shows you how to effectively use principles, practices, and patterns pioneered through the DevOps movement to manage cloud age infrastructure.Ideal for system administrators, infrastructure engineers, team leads, and architects, this book demonstrates various tools, techniques, and patterns you can use to implement infrastructure as code. In three parts, you'll learn about the platforms and tooling involved in creating and configuring infrastructure elements, patterns for using these tools, and practices for making infrastructure as code work in your environment.Examine the pitfalls that organizations fall into when adopting the new generation of infrastructure technologiesUnderstand the capabilities and service models of dynamic infrastructure platformsLearn about tools that provide, provision, and configure core infrastructure resourcesExplore services and tools for managing a dynamic infrastructureLearn specific patterns and practices for provisioning servers, building server templates, and updating running servers

    Understanding Computation explains theoretical computer science in a context you’ll recognize, helping you appreciate why these ideas matter and how they can inform your day-to-day programming.Rather than use mathematical notation or an unfamiliar academic programming language like Haskell or Lisp, this book uses Ruby in a reductionist manner to present formal semantics, automata theory, and functional programming with the lambda calculus. It’s ideal for programmers versed in modern languages, with little or no formal training in computer science.* Understand fundamental computing concepts, such as Turing completeness in languages* Discover how programs use dynamic semantics to communicate ideas to machines* Explore what a computer can do when reduced to its bare essentials* Learn how universal Turing machines led to today’s general-purpose computers* Perform complex calculations, using simple languages and cellular automata* Determine which programming language features are essential for computation* Examine how halting and self-referencing make some computing problems unsolvable* Analyze programs by using abstract interpretation and type systems