Articles by "Computer Engineering"

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Stephen J. Chapman ... 1056 pages - Publisher: McGraw-Hill; 4th edition (March 2017) ... Language: English - ISBN-10: 0073385891 - ISBN-13: 978-0073385891

Fortran for Scientists and Engineers teaches simutaneously both the fundamentals of the Fortran language and a programming style that results in good, maintainable programs. In addition, it serves as a reference for Professionals working in the industry. Among its strengths are its concise, clear explanations of Fortran Syntax and Programming Procedures, the inclusion of a wealth of examples and exercises to help students grasp difficult concepts, and its explanations about how to understand code written for older versions of Fortran.

Ian Chivers, Jane Sleightholme ... 956 pages - Publisher: Springer; 4th edition (August, 2018) ... Language: English - ISBN-10: 3319755013 - ISBN-13: 978-3319755014

This fourth Edition presents new examples on submodules, derived type i/o, object oriented programming, abstract interfaces and procedure pointers, C interop, sorting and searching, statistics and converting to more modern versions of Fortran. Key Features: Highlights the core language features of modern Fortran including data typing, array processing, control structures, functions, subroutines, modules and submodules, user defined types, pointers, operator overloading, generic programming, parallel programming, abstract interfaces, procedure pointers. Pinpoints common problems that occur when programming. Illustrates the use of several compilers. Introduction to Programming with Fortran has been written for the complete beginner with little or no programming background as well as existing Fortran programmers and those with programming experience in other languages.

Ian Chivers, Jane Sleightholme, Balakumar Balachandran, James Duncan, Keith Herold, Gregory Walsh ... 674 pages - Publisher: Springer; 3rd edition (August, 2015) ... Language: English - ISBN-10: 3319177001 - ISBN-13: 978-3319177007

In response to feedback from course delegates this third edition has been revised throughout. It expands on the second edition with new and updated examples in the chapters on arithmetic, i/o, character data, modules, data structuring and generic programming with minor updates to the rest of the chapters. Key Features: Lots of clear, simple examples highlighting the core language features  of modern Fortran including data typing, array processing,  control structures, functions, subroutines, modules,  user defined types, pointers, operator overloading, generic programming, object oriented programming and parallel programming. + Pinpoints common problems that occur when programming + Illustrates the use of several compilers + With better standards conformance in compilers  there are new examples illustrating the following major features: C Interop + IEEE arithmetic + parameterised derived types. Introduction to Programming with Fortran will appeal to the complete beginner, existing. Fortran programmers wishing to update their code and those with programming experience in other languages.

Norman S. Clerman, Walter Spector ... 352 pages - Publisher: Cambridge Univ. Press; 1st edition (December, 2011) ... Language: English - ISBN-10: 052173052X - ISBN-13: 978-0521730525

Fortran is one of the oldest high-level languages and remains the premier language for writing code for science and engineering applications. Modern Fortran: Style and Usage is a book for anyone who uses Fortran, from the novice learner to the advanced expert. It describes best practices for programmers, scientists, engineers, computer scientists, and researchers who want to apply good style and incorporate rigorous usage in their own Fortran code or to establish guidelines for a team project. The presentation concentrates primarily on the characteristics of Fortran 2003, while also describing methods in Fortran 90/95 and valuable new features in Fortran 2008. The authors draw on more than a half century of experience writing production Fortran code to present clear succinct guidelines on formatting, naming, documenting, programming, and packaging conventions and various programming paradigms such as parallel processing (including OpenMP, MPI, and coarrays), OOP, generic programming, and C language interoperability. Programmers working with legacy code will especially appreciate the section on updating old programs.

Benjamin Baka ... 312 pages - Publisher: Packt Publishing; (May, 2017) ... Language: English - ASIN: B01IF7NLM8 by Amazon ...

Data structures allow you to organize data in a particular way efficiently. They are critical to any problem, provide a complete solution, and act like reusable code. In this book, you will learn the essential Python data structures and the most common algorithms. With this easy-to-read book, you will be able to understand the power of linked lists, double linked lists, and circular linked lists. You will be able to create complex data structures such as graphs, stacks and queues. We will explore the application of binary searches and binary search trees. You will learn the common techniques and structures used in tasks such as preprocessing, modeling, and transforming data. We will also discuss how to organize your code in a manageable, consistent, and extendable way. The book will explore in detail sorting algorithms such as bubble sort, selection sort, insertion sort, and merge sort. By the end of the book, you will learn how to build components that are easy to understand, debug, and use in different applications.

Kent D. Lee, Steve Hubbard ... 363 pages - Publisher: Springer; (January, 2015) ... Language: English- ISBN-10: 3319130714 - ISBN-13: 978-3319130712 ...

This textbook explains the concepts and techniques required to write programs that can handle large amounts of data efficiently. Project-oriented and classroom-tested, the book presents a number of important algorithms supported by examples that bring meaning to the problems faced by computer programmers. The idea of computational complexity is also introduced, demonstrating what can and cannot be computed efficiently so that the programmer can make informed judgements about the algorithms they use. Features: includes both introductory and advanced data structures and algorithms topics, with suggested chapter sequences for those respective courses provided in the preface; provides learning goals, review questions and programming exercises in each chapter, as well as numerous illustrative examples; offers downloadable programs and supplementary files at an associated website, with instructor materials available from the author; presents a primer on Python for those from a different language background.

Walter Savitch, Kenrick Mock ... 1272 pages - Publisher: Pearson; 5th edition (March, 2012) ... Language: English - ISBN-10: 0132830310 - ISBN-13: 978-0132830317 ...

Praised for providing an engaging balance of thoughtful examples and explanatory discussion, best-selling author Walter Savitch explains concepts and techniques in a straightforward style using understandable language and code enhanced by a suite of pedagogical tools. Absolute Java is appropriate for both introductory and intermediate programming courses introducing Java.

Narasimha Karumanchi ... 434 pages - Publisher: CareerMonk Plublications; 5th edition (August, 2016) ... Language: English - ISBN-10: 819324527X - ISBN-13: 978-8193245279 ...

A handy guide of sorts for any computer science professional, Data Structures And Algorithms Made Easy: Data Structure And Algorithmic Puzzles is a solution bank for various complex problems related to data structures and algorithms. It can be used as a reference manual by those readers in the computer science industry. The book has around 21 chapters and covers Recursion and Backtracking, Linked Lists, Stacks, Queues,Trees, Priority Queue and Heaps, Disjoint Sets ADT, Graph Algorithms, Sorting, Searching, Selection Algorithms [Medians], Symbol Tables, Hashing, String Algorithms, Algorithms Design Techniques, Greedy Algorithms, Divide and Conquer Algorithms, Dynamic Programming, Complexity Classes, and other Miscellaneous Concepts. Data Structures And Algorithms Made Easy: Data Structure And Algorithmic Puzzles by Narasimha Karumanchi was published in March, and it is coded in C/C++ language. This book serves as guide to prepare for interviews, exams, and campus work. It is also available in Java. In short, this book offers solutions to various complex data structures and algorithmic problems.

Jared P. Lander ... 560 pages - Publisher: Addison-Wesley Professional; 2nd edition (June, 2017) ... Language: English - ISBN-10: 013454692X - ISBN-13: 978-0134546926 ...

Using the open source R language, you can build powerful statistical models to answer many of your most challenging questions. R has traditionally been difficult for non-statisticians to learn, and most R books assume far too much knowledge to be of help. R for Everyone, Second Edition, is the solution. Drawing on his unsurpassed experience teaching new users, professional data scientist Jared P. Lander has written the perfect tutorial for anyone new to statistical programming and modeling. Organized to make learning easy and intuitive, this guide focuses on the 20 percent of R functionality you’ll need to accomplish 80 percent of modern data tasks. Lander’s self-contained chapters start with the absolute basics, offering extensive hands-on practice and sample code. You’ll download and install R; navigate and use the R environment; master basic program control, data import, manipulation, and visualization; and walk through several essential tests. Then, building on this foundation, you’ll construct several complete models, both linear and nonlinear, and use some data mining techniques. After all this you’ll make your code reproducible with LaTeX, RMarkdown, and Shiny. By the time you’re done, you won’t just know how to write R programs, you’ll be ready to tackle the statistical problems you care about most. Coverage includes: Explore R, RStudio, and R packages * Use R for math: variable types, vectors, calling functions, and more * Exploit data structures, including data.frames, matrices, and lists * Read many different types of data * Create attractive, intuitive statistical graphics * Write user-defined functions * Control program flow with if, ifelse, and complex checks * Improve program efficiency with group manipulations * Combine and reshape multiple datasets * Manipulate strings using R’s facilities and regular expressions * Create normal, binomial, and Poisson probability distributions * Build linear, generalized linear, and nonlinear models *Program basic statistics: mean, standard deviation, and t-tests * Train machine learning models * Assess the quality of models and variable selection * Prevent overfitting and perform variable selection, using the Elastic Net and Bayesian methods * Analyze univariate and multivariate time series data * Group data via K-means and hierarchical clustering * Prepare reports, slideshows, and web pages with knitr * Display interactive data with RMarkdown and htmlwidgets * Implement dashboards with Shiny * Build reusable R packages with devtools and Rcpp.

Wes McKinney ... 544 pages - Publisher: O'Reilly Media; 2nd edition (October, 2017) ... Language: English - ISBN-10: 1491957662 - ISBN-13: 978-1491957660 ... 

Get complete instructions for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.6, the second edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You’ll learn the latest versions of pandas, NumPy, IPython, and Jupyter in the process. Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It’s ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub. Use the IPython shell and Jupyter notebook for exploratory computing * Learn basic and advanced features in NumPy (Numerical Python) * Get started with data analysis tools in the pandas library * Use flexible tools to load, clean, transform, merge, and reshape data * Create informative visualizations with matplotlib * Apply the pandas groupby facility to slice, dice, and summarize datasets * Analyze and manipulate regular and irregular time series data * Learn how to solve real-world data analysis problems with thorough, detailed examples.

R. Lyman Ott, Micheal T. Longnecker ... 1297 pages - Publisher: Brooks Cole; 6th edition (December, 2008) ... Language: English - ISBN-10: 0495017582 - ISBN-13: 978-0495017585 ...

Ott and Longnecker's AN INTRODUCTION TO STATISTICAL METHODS AND DATA ANALYSIS, Sixth Edition, provides a broad overview of statistical methods for advanced undergraduate and graduate students from a variety of disciplines who have little or no prior course work in statistics. The authors teach students to solve problems encountered in research projects, to make decisions based on data in general settings both within and beyond the university setting, and to become critical readers of statistical analyses in research papers and in news reports. The first eleven chapters present material typically covered in an introductory statistics course, as well as case studies and examples that are often encountered in undergraduate capstone courses. The remaining chapters cover regression modeling and design of experiments.

Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani ... 426 pages - Publisher: Springer; (September, 2017) ... Language: English - ISBN-10: 1461471370 - ISBN-13: 978-1461471370.

An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.

Mohssen Mohammed,‎ Muhammad Badruddin Khan,‎ Eihab Bashier Mohammed Bashier ... 226 pages - Publisher: CRC Press; 1st edition (July, 2016) ... Language: English - ISBN-10: 1498705383 - ISBN-13: 978-1498705387 ...

Machine learning, one of the top emerging sciences, has an extremely broad range of applications. However, many books on the subject provide only a theoretical approach, making it difficult for a newcomer to grasp the subject material. This book provides a more practical approach by explaining the concepts of machine learning algorithms and describing the areas of application for each algorithm, using simple practical examples to demonstrate each algorithm and showing how different issues related to these algorithms are applied.

Steve Brooks, Andrew Gelman ... 619 pages - Publisher: Chapman and Hall/CRC; (May, 2011) ... Language: English - ISBN-10: 1420079417 - ISBN-13: 978-1420079418.

Since their popularization in the 1990s, Markov chain Monte Carlo (MCMC) methods have revolutionized statistical computing and have had an especially profound impact on the practice of Bayesian statistics. Furthermore, MCMC methods have enabled the development and use of intricate models in an astonishing array of disciplines as diverse as fisheries science and economics. The wide-ranging practical importance of MCMC has sparked an expansive and deep investigation into fundamental Markov chain theory. The Handbook of Markov Chain Monte Carlo provides a reference for the broad audience of developers and users of MCMC methodology interested in keeping up with cutting-edge theory and applications. The first half of the book covers MCMC foundations, methodology, and algorithms. The second half considers the use of MCMC in a variety of practical applications including in educational research, astrophysics, brain imaging, ecology, and sociology. The in-depth introductory section of the book allows graduate students and practicing scientists new to MCMC to become thoroughly acquainted with the basic theory, algorithms, and applications. The book supplies detailed examples and case studies of realistic scientific problems presenting the diversity of methods used by the wide-ranging MCMC community. Those familiar with MCMC methods will find this book a useful refresher of current theory and recent developments.

Tariq Rashid ... 222 pages - Publisher: CreateSpace Independent Publishing Platform; 1st edition (March, 2016) ... Language: English - ISBN-10: 1530826608 - ISBN-13: 978-1530826605 ...

A step-by-step gentle journey through the mathematics of neural networks, and making your own using the Python computer language. Neural networks are a key element of deep learning and artificial intelligence, which today is capable of some truly impressive feats. Yet too few really understand how neural networks actually work. This guide will take you on a fun and unhurried journey, starting from very simple ideas, and gradually building up an understanding of how neural networks work. You won't need any mathematics beyond secondary school, and an accessible introduction to calculus is also included. The ambition of this guide is to make neural networks as accessible as possible to as many readers as possible - there are enough texts for advanced readers already! You'll learn to code in Python and make your own neural network, teaching it to recognise human handwritten numbers, and performing as well as professionally developed networks. Part 1 is about ideas. We introduce the mathematical ideas underlying the neural networks, gently with lots of illustrations and examples. Part 2 is practical. We introduce the popular and easy to learn Python programming language, and gradually builds up a neural network which can learn to recognise human handwritten numbers, easily getting it to perform as well as networks made by professionals. Part 3 extends these ideas further. We push the performance of our neural network to an industry leading 98% using only simple ideas and code, test the network on your own handwriting, take a privileged peek inside the mysterious mind of a neural network, and even get it all working on a Raspberry Pi. All the code in this has been tested to work on a Raspberry Pi Zero.

Karl Hinderer, Ulrich Rieder, Michael Stieglitz ... 530 pages - Publisher: Springer; 1st edition (January, 2017) ... Language: English - ISBN-10: 3319488139 - ISBN-13: 978-3319488134 ...

This book explores discrete-time dynamic optimization and provides a detailed introduction to both deterministic and stochastic models. Covering problems with finite and infinite horizon, as well as Markov renewal programs, Bayesian control models and partially observable processes, the book focuses on the precise modelling of applications in a variety of areas, including operations research, computer science, mathematics, statistics, engineering, economics and finance. Dynamic Optimization is a carefully presented textbook which starts with discrete-time deterministic dynamic optimization problems, providing readers with the tools for sequential decision-making, before proceeding to the more complicated stochastic models. The authors present complete and simple proofs and illustrate the main results with numerous examples and exercises (without solutions). With relevant material covered in four appendices, this book is completely self-contained.

Joseph P. Bigus ... 220 pages - Publisher: Mcgraw-Hill; (May, 1996) ... Language: English - ISBN-10: 0070057796 - ISBN-13: 978-0070057791 ...

Readers will find concrete implementation strategies, reinforced with real-world business examples and a minimum of formulas, and case studies drawn from a broad range of industries. The book illustrates the popular data mining functions of classification, clustering, modeling, and time-series forecasting--through examples developed using the IBM Neural Network Utility.

Simon Haykin ... 842 pages - Publisher: Prentice Hall; 2nd edition (July, 1998) ... Language: English - ISBN-10: 0132733501 - ISBN-13: 978-0132733502 ...

This text represents the first comprehensive treatment of neural networks from an engineering perspective. Thorough, well-organized, and completely up-to-date, it examines all the important aspects of this emerging technology. Neural Networks provides broad coverage of the subject, including the learning process, back propogation, radial basis functions, recurrent networks, self-organizing systems, modular networks, temporal processing, neurodynamics, and VLSI implementations. Chapter objectives, computer experiments, problems, worked examples, a bibliography, photographs, illustrations, and a thorough glossary reinforce key concepts. The author's concise and fluid writing style makes the material more accessible. For graduate-level neural network courses offered in the departments of Computer Engineering, Electrical Engineering, and Computer Science.Renowned for its thoroughness and readability, this well-organized and completely up-to-date text remains the most comprehensive treatment of neural networks from an engineering perspective. Thoroughly revised.

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