The principal enhancement in Java 8 was the addition of functional programming constructs to Java's object-oriented roots. Java 7, 8, and 9 also introduced language features, such as the try-with-resources statement, the diamond operator for generic types, default and static methods in interfaces, the @SafeVarargs annotation, and modules. New library features include pervasive use of functional interfaces and streams, the java.time package for manipulating dates and times, and numerous minor enhancements such as convenience factory methods for collections. In this new edition of Effective Java, Bloch updates the work to take advantage of these new language and library features, and provides specific best practices for their use. Java's increased support for multiple paradigms increases the need for best-practices advice, and this book delivers. As in previous editions, each chapter consists of several "items," each presented in the form of a short, standalone essay that provides specific advice, insight into Java platform subtleties, and updated code examples. The comprehensive descriptions and explanations for each item illuminate what to do, what not to do, and why. Coverage includes:Updated techniques and best practices on classic topics, including objects, classes, methods, libraries, and generics How to avoid the traps and pitfalls of commonly misunderstood subtleties of the platform Focus on the language and its most fundamental libraries, such as java.lang and java.util

    This book draws on material Michael created for his renowned Object Mentor seminars, techniques Michael has used in mentoring to help hundreds of developers, technical managers, and testers bring their legacy systems under control. The topics covered include: Understanding the mechanics of software change, adding features, fixing bugs, improving design, optimizing performance Getting legacy code into a test harness Writing tests that protect you against introducing new problems Techniques that can be used with any language or platform, with examples in Java, C++, C, and C# Accurately identifying where code changes need to be made Coping with legacy systems that aren't object-oriented Handling applications that don't seem to have any structureThis book also includes a catalog of twenty-four dependency-breaking techniques that help you work with program elements in isolation and make safer changes.

    Written from a computer science perspective by three leading experts in the field, it gives an up-to-date treatment of all aspects of the design and implementation of systems for gathering, indexing, and searching documents; methods for evaluating systems; and an introduction to the use of machine learning methods on text collections. All the important ideas are explained using examples and figures, making it perfect for introductory courses in information retrieval for advanced undergraduates and graduate students in computer science. Based on feedback from extensive classroom experience, the book has been carefully structured in order to make teaching more natural and effective. Although originally designed as the primary text for a graduate or advanced undergraduate course in information retrieval, the book will also create a buzz for researchers and professionals alike.

    This highly anticipated fourth edition of the most ...Download Link : readmeaway.com/download?i=0128042915            0128042915 Data Mining: Practical Machine Learning Tools and Techniques (Morgan Kaufmann Series in Data Management Systems) PDF by Ian H. WittenRead Data Mining: Practical Machine Learning Tools and Techniques (Morgan Kaufmann Series in Data Management Systems) PDF from Morgan Kaufmann,Ian H. WittenDownload Ian H. Witten's PDF E-book Data Mining: Practical Machine Learning Tools and Techniques (Morgan Kaufmann Series in Data Management Systems)

    It covers topics ranging from personal responsibility and career development to architectural techniques for keeping your code flexible and easy to adapt and reuse. Read this book, and you'll learn how toFight software rot; Avoid the trap of duplicating knowledge; Write flexible, dynamic, and adaptable code; Avoid programming by coincidence; Bullet-proof your code with contracts, assertions, and exceptions; Capture real requirements; Test ruthlessly and effectively; Delight your users; Build teams of pragmatic programmers; and Make your developments more precise with automation. Written as a series of self-contained sections and filled with entertaining anecdotes, thoughtful examples, and interesting analogies, The Pragmatic Programmer illustrates the best practices and major pitfalls of many different aspects of software development. Whether you're a new coder, an experienced programmer, or a manager responsible for software projects, use these lessons daily, and you'll quickly see improvements in personal productivity, accuracy, and job satisfaction. You'll learn skills and develop habits and attitudes that form the foundation for long-term success in your career. You'll become a Pragmatic Programmer.

    If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination.You'll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas Muller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the math behind them. Familiarity with the NumPy and matplotlib libraries will help you get even more from this book.With this book, you'll learn:Fundamental concepts and applications of machine learningAdvantages and shortcomings of widely used machine learning algorithmsHow to represent data processed by machine learning, including which data aspects to focus onAdvanced methods for model evaluation and parameter tuningThe concept of pipelines for chaining models and encapsulating your workflowMethods for working with text data, including text-specific processing techniquesSuggestions for improving your machine learning and data science skills

    You’ll begin at square one, learning how the Web and web pages work, and then steadily build from there. By the end of the book, you’ll have the skills to create a simple site with multi-column pages that adapt for mobile devices.Learn how to use the latest techniques, best practices, and current web standards—including HTML5 and CSS3. Each chapter provides exercises to help you to learn various techniques, and short quizzes to make sure you understand key concepts.This thoroughly revised edition is ideal for students and professionals of all backgrounds and skill levels, whether you’re a beginner or brushing up on existing skills.Build HTML pages with text, links, images, tables, and formsUse style sheets (CSS) for colors, backgrounds, formatting text, page layout, and even simple animation effectsLearn about the new HTML5 elements, APIs, and CSS3 properties that are changing what you can do with web pagesMake your pages display well on mobile devices by creating a responsive web designLearn how JavaScript works—and why the language is so important in web designCreate and optimize web graphics so they’ll download as quickly as possible

    The commercial success of a game is often dependent upon the quality of the AI, yet the engineering of AI is often begun late in the development process and is frequently misunderstood. In this book, Ian Millington brings extensive professional experience to the problem of improving the quality of AI in games. A game developer since 1987, he was founder of Mindlathe Ltd., at the time the largest specialist AI company in gaming. Ian shows how to think about AI as an integral part of game play. He describes numerous examples from real games and explores the underlying ideas through detailed case studies. He goes further to introduce many techniques little used by developers today. The book's CD-ROM contains a library of C++ source code and demonstration programs, and provides access to a website with a complete commercial source code library of AI algorithms and techniques. * A comprehensive, professional tutorial and reference to implement true AI in games.* Walks through the entire development process from beginning to end.* Includes over 100 pseudo code examples of techniques used in commercial games, case studies for all major genres, a CD-ROM and companion website with extensive C++ source code implementations for Windows, and source code libraries for Linux and OS X available through the website.

    Learn You a Haskell for Great Good! introduces programmers familiar with imperative languages (such as C++, Java, or Python) to the unique aspects of functional programming. Packed with jokes, pop culture references, and the author's own hilarious artwork, Learn You a Haskell for Great Good! eases the learning curve of this complex language, and is a perfect starting point for any programmer looking to expand his or her horizons. The well-known web tutorial on which this book is based is widely regarded as the best way for beginners to learn Haskell, and receives over 30,000 unique visitors monthly.

    This text introduces the basic mathematical and computational methods of theoretical neuroscience and presents applications in a variety of areas including vision, sensory-motor integration, development, learning, and memory.The book is divided into three parts. Part I discusses the relationship between sensory stimuli and neural responses, focusing on the representation of information by the spiking activity of neurons. Part II discusses the modeling of neurons and neural circuits on the basis of cellular and synaptic biophysics. Part III analyzes the role of plasticity in development and learning. An appendix covers the mathematical methods used, and exercises are available on the book's Web site.

    Emphasis is placed on understanding the crisp mathematical idea behind each algorithm, in a manner that is intuitive and rigorous without being unduly formal. Features include: The use of boxes to strengthen the narrative: pieces that provide historical context, descriptions of how the algorithms are used in practice, and excursions for the mathematically sophisticated.Carefully chosen advanced topics that can be skipped in a standard one-semester course, but can be covered in an advanced algorithms course or in a more leisurely two-semester sequence.An accessible treatment of linear programming introduces students to one of the greatest achievements in algorithms. An optional chapter on the quantum algorithm for factoring provides a unique peephole into this exciting topic. In addition to the text, DasGupta also offers a Solutions Manual, which is available on the Online Learning Center.Algorithms is an outstanding undergraduate text, equally informed by the historical roots and contemporary applications of its subject. Like a captivating novel, it is a joy to read. Tim Roughgarden Stanford University

    This makes the book random-access: Items are easy to browse and study in whatever order the reader needs. I will be recommending "Effective Python" to students as an admirably compact source of mainstream advice on a very broad range of topics for the intermediate Python programmer. " Brandon Rhodes, software engineer at Dropbox and chair of PyCon 2016-2017" It s easy to start coding with Python, which is why the language is so popular. However, Python s unique strengths, charms, and expressiveness can be hard to grasp, and there are hidden pitfalls that can easily trip you up. " Effective Python " will help you master a truly Pythonic approach to programming, harnessing Python s full power to write exceptionally robust and well-performing code. Using the concise, scenario-driven style pioneered in Scott Meyers best-selling "Effective C++, " Brett Slatkin brings together 59 Python best practices, tips, and shortcuts, and explains them with realistic code examples. Drawing on years of experience building Python infrastructure at Google, Slatkin uncovers little-known quirks and idioms that powerfully impact code behavior and performance. You ll learn the best way to accomplish key tasks, so you can write code that s easier to understand, maintain, and improve. Key features includeActionable guidelines for all major areas of Python 3.x and 2.x development, with detailed explanations and examples Best practices for writing functions that clarify intention, promote reuse, and avoid bugs Coverage of how to accurately express behaviors with classes and objects Guidance on how to avoid pitfalls with metaclasses and dynamic attributes More efficient approaches to concurrency and parallelism Better techniques and idioms for using Python s built-in modules Tools and best practices for collaborative development Solutions for debugging, testing, and optimization in order to improve quality and performance "

    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.

    But if code isn't clean, it can bring a development organization to its knees. Every year, countless hours and significant resources are lost because of poorly written code. But it doesn't have to be that way. Noted software expert Robert C. Martin presents a revolutionary paradigm with Clean Code: A Handbook of Agile Software Craftsmanship . Martin has teamed up with his colleagues from Object Mentor to distill their best agile practice of cleaning code on the fly into a book that will instill within you the values of a software craftsman and make you a better programmer but only if you work at it. What kind of work will you be doing? You'll be reading code - lots of code. And you will be challenged to think about what's right about that code, and what's wrong with it. More importantly, you will be challenged to reassess your professional values and your commitment to your craft. Clean Code is divided into three parts. The first describes the principles, patterns, and practices of writing clean code. The second part consists of several case studies of increasing complexity. Each case study is an exercise in cleaning up code - of transforming a code base that has some problems into one that is sound and efficient. The third part is the payoff: a single chapter containing a list of heuristics and "smells" gathered while creating the case studies. The result is a knowledge base that describes the way we think when we write, read, and clean code. Readers will come away from this book understanding ‣ How to tell the difference between good and bad code‣ How to write good code and how to transform bad code into good code‣ How to create good names, good functions, good objects, and good classes‣ How to format code for maximum readability ‣ How to implement complete error handling without obscuring code logic ‣ How to unit test and practice test-driven development This book is a must for any developer, software engineer, project manager, team lead, or systems analyst with an interest in producing better code.

    This book is built around the idea that students must study the discipline of psychology as a whole, that the sub-disciplines are intricately connected, and that human behavior is best understood by exploring its functioning state in addition to its potential dysfunctions.