Author: George T. Heineman
Publisher: "O'Reilly Media, Inc."
Release Date: 2016-03-22
Creating robust software requires the use of efficient algorithms, but programmers seldom think about them until a problem occurs. This updated edition of Algorithms in a Nutshell describes a large number of existing algorithms for solving a variety of problems, and helps you select and implement the right algorithm for your needs—with just enough math to let you understand and analyze algorithm performance. With its focus on application, rather than theory, this book provides efficient code solutions in several programming languages that you can easily adapt to a specific project. Each major algorithm is presented in the style of a design pattern that includes information to help you understand why and when the algorithm is appropriate. With this book, you will: Solve a particular coding problem or improve on the performance of an existing solution Quickly locate algorithms that relate to the problems you want to solve, and determine why a particular algorithm is the right one to use Get algorithmic solutions in C, C++, Java, and Ruby with implementation tips Learn the expected performance of an algorithm, and the conditions it needs to perform at its best Discover the impact that similar design decisions have on different algorithms Learn advanced data structures to improve the efficiency of algorithms
Author: Özgür Ergül
Publisher: Springer Science & Business Media
Release Date: 2013-07-23
This easy-to-follow textbook provides a student-friendly introduction to programming and algorithms. Emphasis is placed on the threshold concepts that present barriers to learning, including the questions that students are often too embarrassed to ask. The book promotes an active learning style in which a deeper understanding is gained from evaluating, questioning, and discussing the material, and practised in hands-on exercises. Although R is used as the language of choice for all programs, strict assumptions are avoided in the explanations in order for these to remain applicable to other programming languages. Features: provides exercises at the end of each chapter; includes three mini projects in the final chapter; presents a list of titles for further reading at the end of the book; discusses the key aspects of loops, recursions, program and algorithm efficiency and accuracy, sorting, linear systems of equations, and file processing; requires no prior background knowledge in this area.
Many programmers would love to use Perl for projects that involve heavy lifting, but miss the many traditional algorithms that textbooks teach for other languages. Computer scientists have identified many techniques that a wide range of programs need, such as: Fuzzy pattern matching for text (identify misspellings!) Finding correlations in data Game-playing algorithms Predicting phenomena such as Web traffic Polynomial and spline fitting Using algorithms explained in this book, you too can carry out traditional programming tasks in a high-powered, efficient, easy-to-maintain manner with Perl.This book assumes a basic understanding of Perl syntax and functions, but not necessarily any background in computer science. The authors explain in a readable fashion the reasons for using various classic programming techniques, the kind of applications that use them, and -- most important -- how to code these algorithms in Perl.If you are an amateur programmer, this book will fill you in on the essential algorithms you need to solve problems like an expert. If you have already learned algorithms in other languages, you will be surprised at how much different (and often easier) it is to implement them in Perl. And yes, the book even has the obligatory fractal display program.There have been dozens of books on programming algorithms, some of them excellent, but never before has there been one that uses Perl.The authors include the editor of The Perl Journal and master librarian of CPAN; all are contributors to CPAN and have archived much of the code in this book there."This book was so exciting I lost sleep reading it." Tom Christiansen
Author: José Francisco Martínez-Trinidad
Publisher: Springer Science & Business Media
Release Date: 2006-10-12
This book constitutes the refereed proceedings of the 11th Iberoamerican Congress on Pattern Recognition, CIARP 2006, held in Cancun, Mexico in November 2006. The 99 revised full papers presented together with three keynote articles were carefully reviewed and selected from 239 submissions. The papers cover ongoing research and mathematical methods.
To-the-point, authoritative, no-nonsense solutions have always been a trademark of O'Reilly books. The In a Nutshell books have earned a solid reputation in the field as the well-thumbed references that sit beside the knowledgeable developer's keyboard. C++ in a Nutshell lives up to the In a Nutshell promise. C++ in a Nutshell is a lean, focused reference that offers practical examples for the most important, most often used, aspects of C++.C++ in a Nutshell packs an enormous amount of information on C++ (and the many libraries used with it) in an indispensable quick reference for those who live in a deadline-driven world and need the facts but not the frills.The book's language reference is organized first by topic, followed by an alphabetical reference to the language's keywords, complete with syntax summaries and pointers to the topic references. The library reference is organized by header file, and each library chapter and class declaration presents the classes and types in alphabetical order, for easy lookup. Cross-references link related methods, classes, and other key features. This is an ideal resource for students as well as professional programmers.When you're programming, you need answers to questions about language syntax or parameters required by library routines quickly. What, for example, is the C++ syntax to define an alias for a namespace? Just how do you create and use an iterator to work with the contents of a standard library container? C++ in a Nutshell is a concise desktop reference that answers these questions, putting the full power of this flexible, adaptable (but somewhat difficult to master) language at every C++ programmer's fingertips.
This book offers Python programmers one place to look when they needhelp remembering or deciphering the syntax of this open source languageand its many powerful but scantily documented modules. Thiscomprehensive reference guide makes it easy to look up the mostfrequently needed information--not just about the Python languageitself, but also the most frequently used parts of the standard libraryand the most important third-party extensions. Ask any Python aficionado and you'll hear that Python programmers haveit all: an elegant object-oriented language with readable andmaintainable syntax, that allows for easy integration with componentsin C, C++, Java, or C#, and an enormous collection of precoded standardlibrary and third-party extension modules. Moreover, Python is easy tolearn, yet powerful enough to take on the most ambitious programmingchallenges. But what Python programmers used to lack is a concise andclear reference resource, with the appropriate measure of guidance inhow best to use Python's great power. Python in aNutshell fills this need. Python in a Nutshell, Second Edition covers morethan the language itself; it also deals with the mostfrequently used parts of the standard library, and the most popular andimportant third party extensions. Revised and expanded forPython 2.5, this book now contains the gory details of Python's newsubprocess module and breaking news about Microsoft's newIronPython project. Our "Nutshell" format fits Python perfectly bypresenting the highlights of the most important modules and functionsin its standard library, which cover over 90% of your practicalprogramming needs. This book includes: A fast-paced tutorial on the syntax of the Python language An explanation of object-oriented programming in Python Coverage of iterators, generators, exceptions, modules,packages, strings, and regular expressions A quick reference for Python's built-in types and functionsand key modules Reference material on important third-party extensions,such as Numeric and Tkinter Information about extending and embedding Python Python in a Nutshell provides a solid,no-nonsense quick reference to information that programmers rely on themost. This book will immediately earn its place in any Pythonprogrammer's library. Praise for the First Edition: "In a nutshell, Python in a Nutshell serves oneprimary goal: to act as an immediately accessible goal for the Pythonlanguage. True, you can get most of the same core information that ispresented within the covers of this volume online, but this willinvariably be broken into multiple files, and in all likelihood lackingthe examples or the exact syntax description necessary to trulyunderstand a command." --Richard Cobbett, Linux Format "O'Reilly has several good books, of which Python in aNutshell by Alex Martelli is probably the best for giving yousome idea of what Python is about and how to do useful things with it." --Jerry Pournelle, Byte Magazine
Learn how to apply test-driven development (TDD) to machine-learning algorithms—and catch mistakes that could sink your analysis. In this practical guide, author Matthew Kirk takes you through the principles of TDD and machine learning, and shows you how to apply TDD to several machine-learning algorithms, including Naive Bayesian classifiers and Neural Networks. Machine-learning algorithms often have tests baked in, but they can’t account for human errors in coding. Rather than blindly rely on machine-learning results as many researchers have, you can mitigate the risk of errors with TDD and write clean, stable machine-learning code. If you’re familiar with Ruby 2.1, you’re ready to start. Apply TDD to write and run tests before you start coding Learn the best uses and tradeoffs of eight machine learning algorithms Use real-world examples to test each algorithm through engaging, hands-on exercises Understand the similarities between TDD and the scientific method for validating solutions Be aware of the risks of machine learning, such as underfitting and overfitting data Explore techniques for improving your machine-learning models or data extraction
Author: Hans L. Bodlaender
Release Date: 2013-08-16
This book constitutes the refereed proceedings of the 21st Annual European Symposium on Algorithms, ESA 2013, held in Sophia Antipolis, France, in September 2013 in the context of the combined conference ALGO 2013. The 69 revised full papers presented were carefully reviewed and selected from 303 initial submissions: 53 out of 229 in track "Design and Analysis" and 16 out of 74 in track "Engineering and Applications". The papers in this book present original research in all areas of algorithmic research, including but not limited to: algorithm engineering; algorithmic aspects of networks; algorithmic game theory; approximation algorithms; computational biology; computational finance; computational geometry; combinatorial optimization; data compression; data structures; databases and information retrieval; distributed and parallel computing; graph algorithms; hierarchical memories; heuristics and meta-heuristics; mathematical programming; mobile computing; on-line algorithms; parameterized complexity; pattern matching; quantum computing; randomized algorithms; scheduling and resource allocation problems; streaming algorithms.
Linux deployment continues to increase, and so does the demand for qualified and certified Linux system administrators. If you're seeking a job-based certification from the Linux Professional Institute (LPI), this updated guide will help you prepare for the technically challenging LPIC Level 1 Exams 101 and 102. The third edition of this book is a meticulously researched reference to these exams, written by trainers who work closely with LPI. You'll find an overview of each exam, a summary of the core skills you need, review questions and exercises, as well as a study guide, a practice test, and hints to help you focus. Major topics include: Critical GNU and Unix commands Linux installation and package management Devices and filesystems Text editing, processing, and printing The X Window System Networking and security Mail transfer agents Email, FTP, and web services These exams are for junior to mid-level Linux administrators with about two years of practical system administration experience. You should be comfortable with Linux at the command line and capable of performing simple tasks, including system installation and troubleshooting.
If you’re considering R for statistical computing and data visualization, this book provides a quick and practical guide to just about everything you can do with the open source R language and software environment. You’ll learn how to write R functions and use R packages to help you prepare, visualize, and analyze data. Author Joseph Adler illustrates each process with a wealth of examples from medicine, business, and sports. Updated for R 2.14 and 2.15, this second edition includes new and expanded chapters on R performance, the ggplot2 data visualization package, and parallel R computing with Hadoop. Get started quickly with an R tutorial and hundreds of examples Explore R syntax, objects, and other language details Find thousands of user-contributed R packages online, including Bioconductor Learn how to use R to prepare data for analysis Visualize your data with R’s graphics, lattice, and ggplot2 packages Use R to calculate statistical fests, fit models, and compute probability distributions Speed up intensive computations by writing parallel R programs for Hadoop Get a complete desktop reference to R
This beginning graduate textbook describes both recent achievements and classical results of computational complexity theory. Requiring essentially no background apart from mathematical maturity, the book can be used as a reference for self-study for anyone interested in complexity, including physicists, mathematicians, and other scientists, as well as a textbook for a variety of courses and seminars. More than 300 exercises are included with a selected hint set. The book starts with a broad introduction to the field and progresses to advanced results. Contents include: definition of Turing machines and basic time and space complexity classes, probabilistic algorithms, interactive proofs, cryptography, quantum computation, lower bounds for concrete computational models (decision trees, communication complexity, constant depth, algebraic and monotone circuits, proof complexity), average-case complexity and hardness amplification, derandomization and pseudorandom constructions, and the PCP theorem.
Author: Alex A. Freitas
Publisher: Springer Science & Business Media
Release Date: 2013-11-11
This book integrates two areas of computer science, namely data mining and evolutionary algorithms. Both these areas have become increasingly popular in the last few years, and their integration is currently an active research area. In general, data mining consists of extracting knowledge from data. The motivation for applying evolutionary algorithms to data mining is that evolutionary algorithms are robust search methods which perform a global search in the space of candidate solutions. This book emphasizes the importance of discovering comprehensible, interesting knowledge, which is potentially useful for intelligent decision making. The text explains both basic concepts and advanced topics