الگوهای طراحی توضیح داده‌شده: نگاهی نو به طراحی شیءگرا (ویرایش دوم) (سری الگوهای نرم‌افزاری)

"One of the great things about the book is the way the authors explain concepts very simply using analogies rather than programming examples--this has been very inspiring for a product I'm working on: an audio-only introduction to OOP and software development." --Bruce Eckel "...I would expect that readers with a basic understanding of object-oriented programming and design would find this book useful, before approaching design patterns completely. Design Patterns Explained complements the existing design patterns texts and may perform a very useful role, fitting between introductory texts such as UML Distilled and the more advanced patterns books." --James Noble Leverage the quality and productivity benefits of patterns--without the complexity! Design Patterns Explained, Second Edition is the field's simplest, clearest, most practical introduction to patterns. Using dozens of updated Java examples, it shows programmers and architects exactly how to use patterns to design, develop, and deliver software far more effectively. You'll start with a complete overview of the fundamental principles of patterns, and the role of object-oriented analysis and design in contemporary software development. Then, using easy-to-understand sample code, Alan Shalloway and James Trott illuminate dozens of today's most useful patterns: their underlying concepts, advantages, tradeoffs, implementation techniques, and pitfalls to avoid. Many patterns are accompanied by UML diagrams. Building on their best-selling First Edition, Shalloway and Trott have thoroughly updated this book to reflect new software design trends, patterns, and implementation techniques. Reflecting extensive reader feedback, they have deepened and clarified coverage throughout, and reorganized content for even greater ease of understanding. New and revamped coverage in this edition includes *Better ways to start "thinking in patterns" *How design patterns can facilitate agile development using eXtreme Programming and other methods *How to use commonality and variability analysis to design application architectures *The key role of testing into a patterns-driven development process *How to use factories to instantiate and manage objects more effectively *The Object-Pool Pattern--a new pattern not identified by the "Gang of Four" *New study/practice questions at the end of every chapter Gentle yet thorough, this book assumes no patterns experience whatsoever. It's the ideal "first book" on patterns, and a perfect complement to Gamma's classic Design Patterns. If you're a programmer or architect who wants the clearest possible understanding of design patterns--or if you've struggled to make them work for you--read this book.

"One of the great things about the book is the way the authors explain concepts very simply using analogies rather than programming examples–this has been very inspiring for a product I'm working on: an audio-only introduction to OOP and software development."

–Bruce Eckel

"...I would expect that readers with a basic understanding of object-oriented programming and design would find this book useful, before approaching design patterns completely. Design Patterns Explained complements the existing design patterns texts and may perform a very useful role, fitting between introductory texts such as UML Distilled and the more advanced patterns books."

–James Noble

Leverage the quality and productivity benefits of patterns–without the complexity! Design Patterns Explained, Second Edition is the field's simplest, clearest, most practical introduction to patterns. Using dozens of updated Java examples, it shows programmers and architects exactly how to use patterns to design, develop, and deliver software far more effectively.

You'll start with a complete overview of the fundamental principles of patterns, and the role of object-oriented analysis and design in contemporary software development. Then, using easy-to-understand sample code, Alan Shalloway and James Trott illuminate dozens of today's most useful patterns: their underlying concepts, advantages, tradeoffs, implementation techniques, and pitfalls to avoid. Many patterns are accompanied by UML diagrams.

Building on their best-selling First Edition, Shalloway and Trott have thoroughly updated this book to reflect new software design trends, patterns, and implementation techniques. Reflecting extensive reader feedback, they have deepened and clarified coverage throughout, and reorganized content for even greater ease of understanding. New and revamped coverage in this edition includes

• Better ways to start "thinking in patterns"
• How design patterns can facilitate agile development using eXtreme Programming and other methods
• How to use commonality and variability analysis to design application architectures
• The key role of testing into a patterns-driven development process
• How to use factories to instantiate and manage objects more effectively
• The Object-Pool Pattern–a new pattern not identified by the "Gang of Four"
• New study/practice questions at the end of every chapter

Gentle yet thorough, this book assumes no patterns experience whatsoever. It's the ideal "first book" on patterns, and a perfect complement to Gamma's classic Design Patterns. If you're a programmer or architect who wants the clearest possible understanding of design patterns–or if you've struggled to make them work for you–read this book.

One of the great things about the book is the way the authors explain concepts very simply using analogies rather than programming examples this has been very inspiring for a product I'm working on: an audio-only introduction to OOP and software development." Bruce Eckel ..".I would expect that readers with a basic understanding of object-oriented programming and design would find this book useful, before approaching design patterns completely. "Design Patterns Explained "complements the existing design patterns texts and may perform a very useful role, fitting between introductory texts such as UML Distilled and the more advanced patterns books." James Noble Leverage the quality and productivity benefits of patterns without the complexity! "Design Patterns Explained, Second Edition" is the field's simplest, clearest, most practical introduction to patterns. Using dozens of updated Java examples, it shows programmers and architects exactly how to use patterns to design, develop, and deliver software far more effectively. You'll start with a complete overview of the fundamental principles of patterns, and the role of object-oriented analysis and design in contemporary software development. Then, using easy-to-understand sample code, Alan Shalloway and James Trott illuminate dozens of today's most useful patterns: their underlying concepts, advantages, tradeoffs, implementation techniques, and pitfalls to avoid. Many patterns are accompanied by UML diagrams. Building on their best-selling First Edition, Shalloway and Trott have thoroughly updated this book to reflect new software design trends, patterns, and implementation techniques. Reflecting extensive reader feedback, they have deepened and clarified coverage throughout, and reorganized content for even greater ease of understanding. New and revamped coverage in this edition includes Better ways to start "thinking in patterns" How design patterns can facilitate agile development using eXtreme Programming and other methods How to use commonality and variability analysis to design application architectures The key role of testing into a patterns-driven development process How to use factories to instantiate and manage objects more effectively The Object-Pool Pattern a new pattern not identified by the "Gang of Four" New study/practice questions at the end of every chapter Gentle yet thorough, this book assumes no patterns experience whatsoever. It's the ideal "first book" on patterns, and a perfect complement to Gamma's classic "Design Patterns." If you're a programmer or architect who wants the clearest possible understanding of design patterns or if you've struggled to make them work for you read this book. Introduction. Example : Polynomial Curve Fitting ; Probability Theory ; Model Selection ; The Curse Of Dimensionality Decision Theory ; Information Theory -- Probability Distributions. Binary Vehicles ; Multinomial Variables ; The Gaussian Distribution ; The Exponential Family ; Nonparametric Methods -- Linear Models For Regression. Linear Basis Function Models ; The Bias-variance Decomposition ; Bayesian Linear Regression ; Bayesian Model Comparison ; The Evidence Approximation ; Limitations Of Fixed Basis Functions -- Linear Models For Classification. Discriminant Functions ; Probabilistic Generative Models ; Probabilistic Discrimitive Models ; The Laplace Approximation ; Bayesian Logistic Regression -- Neural Networks. Feed-forward Network Functions ; Network Training ; Error Backpropagation ; The Hessian Matrix ; Regularization In Neural Networks ; Mixture Density Networks ; Bayesian Neural Networks. Kernel Methods. Dual Representations ; Constructing Kernals ; Radial Basis Function Networks ; Gaussian Processes -- Sparse Kernel Machines. Maximum Margin Classifiers ; Relevance Vector Machines -- Graphical Models. Bayesian Networks ; Conditional Independence ; Markov Random Fields ; Inference In Graphical Models -- Mixture Models And Em. K-means Clustering ; Mixtures Of Gaussians ; An Alternative View Of Em ; The Em Algorithm In General -- Approximate Inference. Variational Inference ; Illustration : Variational Mixture Of Gaussians ; Variational Linear Regression ; Exponential Family Distributions ; Local Variational Methods ; Variational Logistic Regression ; Expectation Propagation -- Sampling Methods. Basic Sampling Algorithms ; Markov Chain Monte Carlo ; Gibbs Sampling ; Slice Sampling ; The Hybrid Monte Carlo Algorithm ; Estimating The Partition Function. Continuous Latent Variables. Principal Component Analysis ; Probabilistic Pca ; Kernel Pca ; Nonlinear Latent Variable Models -- Sequential Data. Markoc Models ; Hidden Markov Models ; Linear Dynamical Systems -- Combining Models. Bayesian Model Averaging ; Committees ; Boosting ; Tree-based Models ; Conditional Mixture Models -- Data Sets -- Probability Distributions -- Properties Of Matrices -- Calculus Of Variations -- Lagrange Multipliers. Christopher M. Bishop. Includes Bibliographical References (p. 711-728) And Index. The dramatic growth in practical applications for machine learning over the last ten years has been accompanied by many important developments in the underlying algorithms and techniques. For example, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic techniques. The practical applicability of Bayesian methods has been greatly enhanced by the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation, while new models based on kernels have had a significant impact on both algorithms and applications. This completely new textbook reflects these recent developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first-year PhD students, as well as researchers and practitioners. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory. The book is suitable for courses on machine learning, statistics, computer science, signal processing, computer vision, data mining, and bioinformatics. Extensive support is provided for course instructors, including more than 400 exercises, graded according to difficulty. Example solutions for a subset of the exercises are available from the book web site, while solutions for the remainder can be obtained by instructors from the publisher. The book is supported by a great deal of additional material, and the reader is encouraged to visit the book web site for the latest information. Christopher M. Bishop is Deputy Director of Microsoft Research Cambridge, and holds a Chair in Computer Science at the University of Edinburgh. He is a Fellow of Darwin College Cambridge, a Fellow of the Royal Academy of Engineering, and a Fellow of the Royal Society of Edinburgh. His previous textbook "Neural Networks for Pattern Recognition" has been widely adopted Pattern recognition has its origins in engineering, whereas machine learning grew out of computer science. However, these activities can be viewed as two facets of the same field, and together they have undergone substantial development over the past ten years. In particular, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic models. Also, the practical applicability of Bayesian methods has been greatly enhanced through the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation. Similarly, new models based on kernels have had a significant impact on both algorithms and applications. This new textbook reflects these recent developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first-year PhD students, as well as researchers and practitioners, and assumes no previous knowledge of pattern recognition or machine learning concepts. Knowledge of multivariate calculus and basic linear algebra is required, and some familiarity with probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory. This guide for software developers describes the basic principles underlying object-oriented programming and design patterns, beginning with introductory material on object-oriented software development and the limitations of traditional object-oriented design, and ending with an exploration of ten commonly used design patterns, such as the facade, adapter, bridge, observer, double-checked locking, and factory method patterns. Original. (Advanced) Leverage the quality and productivity benefits of patterns--without the complexity. This second edition is the field's simplest, clearest, most practical introduction to patterns. Using dozens of new C# and updated Java examples, it shows programmers and architects exactly how to use patterns to design, develop, and deliver software far more effectively The field of pattern recognition has undergone substantial development over the years. This book reflects these developments while providing a grounding in the basic concepts of pattern recognition and machine learning. It is aimed at advanced undergraduates or first year PhD students, as well as researchers and practitioners