It is designed to be used in conjunction with code provided on an online repository. The book complements and explains the raw code in a way that is accessible to anyone who regularly practices the art of programming. The book can also be used in advanced programming courses in computer science and software engineering programs.The book contains 33 different styles for writing the term frequency task. The styles are grouped into nine categories: historical, basic, function composition, objects and object interactions, reflection and metaprogramming, adversity, data-centric, concurrency, and interactivity. The author verbalizes the constraints in each style and explains the example programs. Each chapter first presents the constraints of the style, next shows an example program, and then gives a detailed explanation of the code. Most chapters also have sections focusing on the use of the style in systems design as well as sections describing the historical context in which the programming style emerged.

    Now featuring more than 250 color illustrations throughout, this perennially popular guide is a must for novices who want to work with HTML or XHTML, which continue to be the foundation for any Web site The new edition features nearly 50 percent new and updated content, including expanded coverage of CSS and scripting, new coverage of syndication and podcasting, and new sample HTML projects, including a personal Web page, an eBay auction page, a company Web site, and an online product catalog The companion Web site features an eight-page expanded Cheat Sheet with ready-reference information on commands, syntax, colors, CSS elements, and more Covers planning a Web site, formatting Web pages, using CSS, getting creative with colors and fonts, managing layouts, and integrating scripts

    The author has sought to utlilize the technology now available for the teaching and learning of calculus. The hand-held graphics calculator is one such form of technology that has been integrated into the book. Topics in algebra, trigonometry, and analytical geometry appear in the Appendix.

    Even for people not working with the Unified Process, the book is still of great use. UML 2 and the Unified Process, Second Edition is a must-read for every UML 2 beginner and a helpful guide and reference for the experienced practitioner."--Roland Leibundgut, Technical Director, Zuehlke Engineering Ltd."This book is a good starting point for organizations and individuals who are adopting UP and need to understand how to provide visualization of the different aspects needed to satisfy it. "--Eric Naiburg, Market Manager, Desktop Products, IBM Rational SoftwareThis thoroughly revised edition provides an indispensable and practical guide to the complex process of object-oriented analysis and design using UML 2. It describes how the process of OO analysis and design fits into the software development lifecycle as defined by the Unified Process (UP). UML 2 and the Unified Process contains a wealth of practical, powerful, and useful techniques that you can apply immediately. As you progress through the text, you will learn OO analysis and design techniques, UML syntax and semantics, and the relevant aspects of the UP. The book provides you with an accurate and succinct summary of both UML and UP from the point of view of the OO analyst and designer.This book providesChapter roadmaps, detailed diagrams, and margin notes allowing you to focus on your needs Outline summaries for each chapter, making it ideal for revision, and a comprehensive index that can be used as a reference New to this edition:Completely revised and updated for UML 2 syntax Easy to understand explanations of the new UML 2 semantics More real-world examples A new section on the Object Constraint Language (OCL) Introductory material on the OMG's Model Driven Architecture (MDA) The accompanying website providesA complete example of a simple e-commerce system Open source tools for requirements engineering and use case modeling Industrial-strength UML course materials based on the book

    It is the basis for a new approach to artificial intelligence that aims to program computers to use example data or past experience to solve a given problem. In this volume in the MIT Press Essential Knowledge series, Ethem Alpayd�n offers a concise and accessible overview of the new AI. This expanded edition offers new material on such challenges facing machine learning as privacy, security, accountability, and bias. Alpayd�n, author of a popular textbook on machine learning, explains that as Big Data has gotten bigger, the theory of machine learning--the foundation of efforts to process that data into knowledge--has also advanced. He describes the evolution of the field, explains important learning algorithms, and presents example applications. He discusses the use of machine learning algorithms for pattern recognition; artificial neural networks inspired by the human brain; algorithms that learn associations between instances; and reinforcement learning, when an autonomous agent learns to take actions to maximize reward. In a new chapter, he considers transparency, explainability, and fairness, and the ethical and legal implications of making decisions based on data.

    You'll learn techniques for implementing specific Web technologies and patterns to solve the needs of a typical company as it grows from modest beginnings to become a global enterprise.Learn basic Web techniques for application integrationUse HTTP and the Web’s infrastructure to build scalable, fault-tolerant enterprise applicationsDiscover the Create, Read, Update, Delete (CRUD) pattern for manipulating resourcesBuild RESTful services that use hypermedia to model state transitions and describe business protocolsLearn how to make Web-based solutions secure and interoperableExtend integration patterns for event-driven computing with the Atom Syndication Format and implement multi-party interactions in AtomPubUnderstand how the Semantic Web will impact systems design

    Two leaders in the requirements community have teamed up to deliver a contemporary set of practices covering the full range of requirements development and management activities on software projects. Describes practical, effective, field-tested techniques for managing the requirements engineering process from end to end. Provides examples demonstrating how requirements "good practices" can lead to fewer change requests, higher customer satisfaction, and lower development costs. Fully updated with contemporary examples and many new practices and techniques. Describes how to apply effective requirements practices to agile projects and numerous other special project situations. Targeted to business analysts, developers, project managers, and other software project stakeholders who have a general understanding of the software development process. Shares the insights gleaned from the authors' extensive experience delivering hundreds of software-requirements training courses, presentations, and webinars.New chapters are included on specifying data requirements, writing high-quality functional requirements, and requirements reuse. Considerable depth has been added on business requirements, elicitation techniques, and nonfunctional requirements. In addition, new chapters recommend effective requirements practices for various special project situations, including enhancement and replacement, packaged solutions, outsourced, business process automation, analytics and reporting, and embedded and other real-time systems projects.

    If your job requires you to manage and analyze all kinds of data, turn to Head First Data Analysis, where you'll quickly learn how to collect and organize data, sort the distractions from the truth, find meaningful patterns, draw conclusions, predict the future, and present your findings to others. Whether you're a product developer researching the market viability of a new product or service, a marketing manager gauging or predicting the effectiveness of a campaign, a salesperson who needs data to support product presentations, or a lone entrepreneur responsible for all of these data-intensive functions and more, the unique approach in Head First Data Analysis is by far the most efficient way to learn what you need to know to convert raw data into a vital business tool. You'll learn how to:Determine which data sources to use for collecting information Assess data quality and distinguish signal from noise Build basic data models to illuminate patterns, and assimilate new information into the models Cope with ambiguous information Design experiments to test hypotheses and draw conclusions Use segmentation to organize your data within discrete market groups Visualize data distributions to reveal new relationships and persuade others Predict the future with sampling and probability models Clean your data to make it useful Communicate the results of your analysis to your audience Using the latest research in cognitive science and learning theory to craft a multi-sensory learning experience, Head First Data Analysis uses a visually rich format designed for the way your brain works, not a text-heavy approach that puts you to sleep.

    Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning.The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models.Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.

    It describes how to index your data, including types you definitely need to know such as MS Word, PDF, HTML, and XML. It introduces you to searching, sorting, filtering, and highlighting search results.Lucene powers search in surprising placesWhat's Inside- How to integrate Lucene into your applications- Ready-to-use framework for rich document handling- Case studies including Nutch, TheServerSide, jGuru, etc.- Lucene ports to Perl, Python, C#/.Net, and C++- Sorting, filtering, term vectors, multiple, and remote index searching- The new SpanQuery family, extending query parser, hit collecting- Performance testing and tuning- Lucene add-ons (hit highlighting, synonym lookup, and others)

    The book covers lambdas, streams, and functional-style programming. With Java 8's functional features you can now write more concise code in less time, and also automatically benefit from multicore architectures. It's time to dig in!

    You'll also learn how to make your programs interactive and how to test your code safely before adding it to a project. In the second half of the book, you'll put your new knowledge into practice with three substantial projects: a Space Invaders-inspired arcade game, data visualizations with Python's super-handy libraries, and a simple web app you can deploy online.As you work through Python Crash Course, you'll learn how to: Use powerful Python libraries and tools, including matplotlib, NumPy, and PygalMake 2D games that respond to keypresses and mouse clicks, and that grow more difficult as the game progressesWork with data to generate interactive visualizationsCreate and customize simple web apps and deploy them safely onlineDeal with mistakes and errors so you can solve your own programming problemsIf you've been thinking seriously about digging into programming, Python Crash Course will get you up to speed and have you writing real programs fast. Why wait any longer? Start your engines and code!

    The focus is on algorithms and hence the book is well suited for students in computer science and engineering. Motivation is provided from the application areas: all solutions and techniques from computational geometry are related to particular applications in robotics, graphics, CAD/CAM, and geographic information systems. For students this motivation will be especially welcome. Modern insights in computational geometry are used to provide solutions that are both efficient and easy to understand and implement. All the basic techniques and topics from computational geometry, as well as several more advanced topics, are covered. The book is largely self-contained and can be used for self-study by anyone with a basic background in algorithms. In the second edition, besides revisions to the first edition, a number of new exercises have been added.

    Many Python developers are now taking an interest in high-level software architecture patterns such as hexagonal/clean architecture, event-driven architecture, and strategic patterns prescribed by domain-driven design (DDD). But translating those patterns into Python isn't always straightforward.With this practical guide, Harry Percival and Bob Gregory from MADE.com introduce proven architectural design patterns to help Python developers manage application complexity. Each pattern is illustrated with concrete examples in idiomatic Python that explain how to avoid some of the unnecessary verbosity of Java and C# syntax. You'll learn how to implement each of these patterns in a Pythonic way.Architectural design patterns include:Dependency inversion, and its links to ports and adapters (hexagonal/clean architecture)Domain-driven design's distinction between entities, value objects, and aggregatesRepository and Unit of Work patterns for persistent storageEvents, commands, and the message busCommand Query Responsibility Segregation (CQRS)Event-driven architecture and reactive microservices

    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