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Mastering Algorithms With C
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Book Synopsis Mastering Algorithms with C by : Kyle Loudon
Download or read book Mastering Algorithms with C written by Kyle Loudon and published by "O'Reilly Media, Inc.". This book was released on 1999 with total page 560 pages. Available in PDF, EPUB and Kindle. Book excerpt: Implementations, as well as interesting, real-world examples of each data structure and algorithm, are shown in the text. Full source code appears on the accompanying disk.
Book Synopsis Mastering Algorithms with C by : Kyle Loudon
Download or read book Mastering Algorithms with C written by Kyle Loudon and published by "O'Reilly Media, Inc.". This book was released on 1999-08-05 with total page 564 pages. Available in PDF, EPUB and Kindle. Book excerpt: There are many books on data structures and algorithms, including some with useful libraries of C functions. Mastering Algorithms with C offers you a unique combination of theoretical background and working code. With robust solutions for everyday programming tasks, this book avoids the abstract style of most classic data structures and algorithms texts, but still provides all of the information you need to understand the purpose and use of common programming techniques. Implementations, as well as interesting, real-world examples of each data structure and algorithm, are included. Using both a programming style and a writing style that are exceptionally clean, Kyle Loudon shows you how to use such essential data structures as lists, stacks, queues, sets, trees, heaps, priority queues, and graphs. He explains how to use algorithms for sorting, searching, numerical analysis, data compression, data encryption, common graph problems, and computational geometry. And he describes the relative efficiency of all implementations. The compression and encryption chapters not only give you working code for reasonably efficient solutions, they offer explanations of concepts in an approachable manner for people who never have had the time or expertise to study them in depth. Anyone with a basic understanding of the C language can use this book. In order to provide maintainable and extendible code, an extra level of abstraction (such as pointers to functions) is used in examples where appropriate. Understanding that these techniques may be unfamiliar to some programmers, Loudon explains them clearly in the introductory chapters. Contents include: Pointers Recursion Analysis of algorithms Data structures (lists, stacks, queues, sets, hash tables, trees, heaps, priority queues, graphs) Sorting and searching Numerical methods Data compression Data encryption Graph algorithms Geometric algorithms
Book Synopsis Mastering Algorithms With C (Includes Diskette) by : Kyle Loudon
Download or read book Mastering Algorithms With C (Includes Diskette) written by Kyle Loudon and published by . This book was released on 1999-01-01 with total page 572 pages. Available in PDF, EPUB and Kindle. Book excerpt: There are many books on data structures and algorithms, and some books laying out useful libraries of C functions, but this book aims to give you a unique combination of theoretical background and working code. In offering robust solutions for everyday programming tasks,
Book Synopsis Mastering Algorithms with Perl by : Jarkko Hietaniemi
Download or read book Mastering Algorithms with Perl written by Jarkko Hietaniemi and published by "O'Reilly Media, Inc.". This book was released on 1999-08-18 with total page 708 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Book Synopsis Mastering the C++17 STL by : Arthur O'Dwyer
Download or read book Mastering the C++17 STL written by Arthur O'Dwyer and published by Packt Publishing Ltd. This book was released on 2017-09-28 with total page 379 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book breaks down the C++ STL, teaching you how to extract its gems and apply them to your programming. About This Book Boost your productivity as a C++ developer with the latest features of C++17 Develop high-quality, fast, and portable applications with the varied features of the STL Migrate from older versions (C++11, C++14) to C++17 Who This Book Is For This book is for developers who would like to master the C++ STL and make full use of its components. Prior C++ knowledge is assumed. What You Will Learn Make your own iterator types, allocators, and thread pools. Master every standard container and every standard algorithm. Improve your code by replacing new/delete with smart pointers. Understand the difference between monomorphic algorithms, polymorphic algorithms, and generic algorithms. Learn the meaning and applications of vocabulary type, product type and sum type. In Detail Modern C++ has come a long way since 2011. The latest update, C++17, has just been ratified and several implementations are on the way. This book is your guide to the C++ standard library, including the very latest C++17 features. The book starts by exploring the C++ Standard Template Library in depth. You will learn the key differences between classical polymorphism and generic programming, the foundation of the STL. You will also learn how to use the various algorithms and containers in the STL to suit your programming needs. The next module delves into the tools of modern C++. Here you will learn about algebraic types such as std::optional, vocabulary types such as std::function, smart pointers, and synchronization primitives such as std::atomic and std::mutex. In the final module, you will learn about C++'s support for regular expressions and file I/O. By the end of the book you will be proficient in using the C++17 standard library to implement real programs, and you'll have gained a solid understanding of the library's own internals. Style and approach This book takes a concise but comprehensive approach to explaining and applying the C++ STL, one feature at a time.
Book Synopsis Mastering Machine Learning Algorithms by : Giuseppe Bonaccorso
Download or read book Mastering Machine Learning Algorithms written by Giuseppe Bonaccorso and published by Packt Publishing Ltd. This book was released on 2018-05-25 with total page 567 pages. Available in PDF, EPUB and Kindle. Book excerpt: Explore and master the most important algorithms for solving complex machine learning problems. Key Features Discover high-performing machine learning algorithms and understand how they work in depth. One-stop solution to mastering supervised, unsupervised, and semi-supervised machine learning algorithms and their implementation. Master concepts related to algorithm tuning, parameter optimization, and more Book Description Machine learning is a subset of AI that aims to make modern-day computer systems smarter and more intelligent. The real power of machine learning resides in its algorithms, which make even the most difficult things capable of being handled by machines. However, with the advancement in the technology and requirements of data, machines will have to be smarter than they are today to meet the overwhelming data needs; mastering these algorithms and using them optimally is the need of the hour. Mastering Machine Learning Algorithms is your complete guide to quickly getting to grips with popular machine learning algorithms. You will be introduced to the most widely used algorithms in supervised, unsupervised, and semi-supervised machine learning, and will learn how to use them in the best possible manner. Ranging from Bayesian models to the MCMC algorithm to Hidden Markov models, this book will teach you how to extract features from your dataset and perform dimensionality reduction by making use of Python-based libraries such as scikit-learn. You will also learn how to use Keras and TensorFlow to train effective neural networks. If you are looking for a single resource to study, implement, and solve end-to-end machine learning problems and use-cases, this is the book you need. What you will learn Explore how a ML model can be trained, optimized, and evaluated Understand how to create and learn static and dynamic probabilistic models Successfully cluster high-dimensional data and evaluate model accuracy Discover how artificial neural networks work and how to train, optimize, and validate them Work with Autoencoders and Generative Adversarial Networks Apply label spreading and propagation to large datasets Explore the most important Reinforcement Learning techniques Who this book is for This book is an ideal and relevant source of content for data science professionals who want to delve into complex machine learning algorithms, calibrate models, and improve the predictions of the trained model. A basic knowledge of machine learning is preferred to get the best out of this guide.
Book Synopsis Secure Programming with Static Analysis by : Brian Chess
Download or read book Secure Programming with Static Analysis written by Brian Chess and published by Pearson Education. This book was released on 2007-06-29 with total page 1101 pages. Available in PDF, EPUB and Kindle. Book excerpt: The First Expert Guide to Static Analysis for Software Security! Creating secure code requires more than just good intentions. Programmers need to know that their code will be safe in an almost infinite number of scenarios and configurations. Static source code analysis gives users the ability to review their work with a fine-toothed comb and uncover the kinds of errors that lead directly to security vulnerabilities. Now, there’s a complete guide to static analysis: how it works, how to integrate it into the software development processes, and how to make the most of it during security code review. Static analysis experts Brian Chess and Jacob West look at the most common types of security defects that occur today. They illustrate main points using Java and C code examples taken from real-world security incidents, showing how coding errors are exploited, how they could have been prevented, and how static analysis can rapidly uncover similar mistakes. This book is for everyone concerned with building more secure software: developers, security engineers, analysts, and testers.
Book Synopsis Mastering Machine Learning with scikit-learn by : Gavin Hackeling
Download or read book Mastering Machine Learning with scikit-learn written by Gavin Hackeling and published by Packt Publishing Ltd. This book was released on 2017-07-24 with total page 249 pages. Available in PDF, EPUB and Kindle. Book excerpt: Use scikit-learn to apply machine learning to real-world problems About This Book Master popular machine learning models including k-nearest neighbors, random forests, logistic regression, k-means, naive Bayes, and artificial neural networks Learn how to build and evaluate performance of efficient models using scikit-learn Practical guide to master your basics and learn from real life applications of machine learning Who This Book Is For This book is intended for software engineers who want to understand how common machine learning algorithms work and develop an intuition for how to use them, and for data scientists who want to learn about the scikit-learn API. Familiarity with machine learning fundamentals and Python are helpful, but not required. What You Will Learn Review fundamental concepts such as bias and variance Extract features from categorical variables, text, and images Predict the values of continuous variables using linear regression and K Nearest Neighbors Classify documents and images using logistic regression and support vector machines Create ensembles of estimators using bagging and boosting techniques Discover hidden structures in data using K-Means clustering Evaluate the performance of machine learning systems in common tasks In Detail Machine learning is the buzzword bringing computer science and statistics together to build smart and efficient models. Using powerful algorithms and techniques offered by machine learning you can automate any analytical model. This book examines a variety of machine learning models including popular machine learning algorithms such as k-nearest neighbors, logistic regression, naive Bayes, k-means, decision trees, and artificial neural networks. It discusses data preprocessing, hyperparameter optimization, and ensemble methods. You will build systems that classify documents, recognize images, detect ads, and more. You will learn to use scikit-learn's API to extract features from categorical variables, text and images; evaluate model performance, and develop an intuition for how to improve your model's performance. By the end of this book, you will master all required concepts of scikit-learn to build efficient models at work to carry out advanced tasks with the practical approach. Style and approach This book is motivated by the belief that you do not understand something until you can describe it simply. Work through toy problems to develop your understanding of the learning algorithms and models, then apply your learnings to real-life problems.
Book Synopsis Understanding and Using C Pointers by : Richard M Reese
Download or read book Understanding and Using C Pointers written by Richard M Reese and published by "O'Reilly Media, Inc.". This book was released on 2013-05-01 with total page 300 pages. Available in PDF, EPUB and Kindle. Book excerpt: Improve your programming through a solid understanding of C pointers and memory management. With this practical book, you’ll learn how pointers provide the mechanism to dynamically manipulate memory, enhance support for data structures, and enable access to hardware. Author Richard Reese shows you how to use pointers with arrays, strings, structures, and functions, using memory models throughout the book. Difficult to master, pointers provide C with much flexibility and power—yet few resources are dedicated to this data type. This comprehensive book has the information you need, whether you’re a beginner or an experienced C or C++ programmer or developer. Get an introduction to pointers, including the declaration of different pointer types Learn about dynamic memory allocation, de-allocation, and alternative memory management techniques Use techniques for passing or returning data to and from functions Understand the fundamental aspects of arrays as they relate to pointers Explore the basics of strings and how pointers are used to support them Examine why pointers can be the source of security problems, such as buffer overflow Learn several pointer techniques, such as the use of opaque pointers, bounded pointers and, the restrict keyword
Book Synopsis Mastering Machine Learning Algorithms by : Giuseppe Bonaccorso
Download or read book Mastering Machine Learning Algorithms written by Giuseppe Bonaccorso and published by Packt Publishing Ltd. This book was released on 2020-01-31 with total page 799 pages. Available in PDF, EPUB and Kindle. Book excerpt: Updated and revised second edition of the bestselling guide to exploring and mastering the most important algorithms for solving complex machine learning problems Key FeaturesUpdated to include new algorithms and techniquesCode updated to Python 3.8 & TensorFlow 2.x New coverage of regression analysis, time series analysis, deep learning models, and cutting-edge applicationsBook Description Mastering Machine Learning Algorithms, Second Edition helps you harness the real power of machine learning algorithms in order to implement smarter ways of meeting today's overwhelming data needs. This newly updated and revised guide will help you master algorithms used widely in semi-supervised learning, reinforcement learning, supervised learning, and unsupervised learning domains. You will use all the modern libraries from the Python ecosystem – including NumPy and Keras – to extract features from varied complexities of data. Ranging from Bayesian models to the Markov chain Monte Carlo algorithm to Hidden Markov models, this machine learning book teaches you how to extract features from your dataset, perform complex dimensionality reduction, and train supervised and semi-supervised models by making use of Python-based libraries such as scikit-learn. You will also discover practical applications for complex techniques such as maximum likelihood estimation, Hebbian learning, and ensemble learning, and how to use TensorFlow 2.x to train effective deep neural networks. By the end of this book, you will be ready to implement and solve end-to-end machine learning problems and use case scenarios. What you will learnUnderstand the characteristics of a machine learning algorithmImplement algorithms from supervised, semi-supervised, unsupervised, and RL domainsLearn how regression works in time-series analysis and risk predictionCreate, model, and train complex probabilistic models Cluster high-dimensional data and evaluate model accuracy Discover how artificial neural networks work – train, optimize, and validate them Work with autoencoders, Hebbian networks, and GANsWho this book is for This book is for data science professionals who want to delve into complex ML algorithms to understand how various machine learning models can be built. Knowledge of Python programming is required.
Book Synopsis Introduction to Recursive Programming by : Manuel Rubio-Sanchez
Download or read book Introduction to Recursive Programming written by Manuel Rubio-Sanchez and published by CRC Press. This book was released on 2017-10-05 with total page 451 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recursion is one of the most fundamental concepts in computer science and a key programming technique that allows computations to be carried out repeatedly. Despite the importance of recursion for algorithm design, most programming books do not cover the topic in detail, despite the fact that numerous computer programming professors and researchers in the field of computer science education agree that recursion is difficult for novice students. Introduction to Recursive Programming provides a detailed and comprehensive introduction to recursion. This text will serve as a useful guide for anyone who wants to learn how to think and program recursively, by analyzing a wide variety of computational problems of diverse difficulty. It contains specific chapters on the most common types of recursion (linear, tail, and multiple), as well as on algorithm design paradigms in which recursion is prevalent (divide and conquer, and backtracking). Therefore, it can be used in introductory programming courses, and in more advanced classes on algorithm design. The book also covers lower-level topics related to iteration and program execution, and includes a rich chapter on the theoretical analysis of the computational cost of recursive programs, offering readers the possibility to learn some basic mathematics along the way. It also incorporates several elements aimed at helping students master the material. First, it contains a larger collection of simple problems in order to provide a solid foundation of the core concepts, before diving into more complex material. In addition, one of the book's main assets is the use of a step-by-step methodology, together with specially designed diagrams, for guiding and illustrating the process of developing recursive algorithms. Furthermore, the book covers combinatorial problems and mutual recursion. These topics can broaden students' understanding of recursion by forcing them to apply the learned concepts differently, or in a more sophisticated manner. The code examples have been written in Python 3, but should be straightforward to understand for students with experience in other programming languages. Finally, worked out solutions to over 120 end-of-chapter exercises are available for instructors.
Book Synopsis Mastering Algorithms with C : by : Harry. H. Chaudhary.
Download or read book Mastering Algorithms with C : written by Harry. H. Chaudhary. and published by Createspace LLC USA. This book was released on 2014-06-15 with total page 245 pages. Available in PDF, EPUB and Kindle. Book excerpt: Essential Data Structures Skills -- Made Easy! This book gives a good start and Complete introduction for data structures and algorithms for Beginner’s. While reading this book it is fun and easy to read it. This book is best suitable for first time DSA readers, Covers all fast track topics of DSA for all Computer Science students and Professionals. Data Structures and Other Objects Using C or C++ takes a gentle approach to the data structures course in C Providing an early, text gives students a firm grasp of key concepts and allows those experienced in another language to adjust easily. Flexible by design,. Finally, a solid foundation in building and using abstract data types is also provided. Using C, this book develops the concepts and theory of data structures and algorithm analysis in a gradual, step-by-step manner, proceeding from concrete examples to abstract principles. Standish covers a wide range of Both traditional and contemporary software engineering topics. This is a handy guide of sorts for any computer science engineering Students, Data Structures And Algorithms is a solution bank for various complex problems related to data structures and algorithms. It can be used as a reference manual by Computer Science Engineering students. this Book also covers all aspects of B.TECH CS,IT, and BCA and MCA, BSC IT. || Inside Chapters. || ============== 1 Introduction. 2 Array. 3 Matrix . 4 Sorting . 5 Stack. 6 Queue. 7 Linked List. 8 Tree. 9 Graph . 10 Hashing. 11 Algorithms. 12 Misc. Topics. 13 Problems.
Book Synopsis String Algorithms in C by : Thomas Mailund
Download or read book String Algorithms in C written by Thomas Mailund and published by Apress. This book was released on 2020-11-12 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Implement practical data structures and algorithms for text search and discover how it is used inside other larger applications. This unique in-depth guide explains string algorithms using the C programming language. String Algorithms in C teaches you the following algorithms and how to use them: classical exact search algorithms; tries and compact tries; suffix trees and arrays; approximative pattern searches; and more. In this book, author Thomas Mailund provides a library with all the algorithms and applicable source code that you can use in your own programs. There are implementations of all the algorithms presented in this book so there are plenty of examples. You’ll understand that string algorithms are used in various applications such as image processing, computer vision, text analytics processing from data science to web applications, information retrieval from databases, network security, and much more. What You Will Learn Use classical exact search algorithms including naive search, borders/border search, Knuth-Morris-Pratt, and Boyer-Moor with or without Horspool Search in trees, use tries and compact tries, and work with the Aho-Carasick algorithm Process suffix trees including the use and development of McCreight’s algorithm Work with suffix arrays including binary searches; sorting naive constructions; suffix tree construction; skew algorithms; and the Borrows-Wheeler transform (BWT) Deal with enhanced suffix arrays including longest common prefix (LCP) Carry out approximative pattern searches among suffix trees and approximative BWT searches Who This Book Is For Those with at least some prior programming experience with C or Assembly and have at least prior experience with programming algorithms.
Book Synopsis Algorithms in C by : Robert Sedgewick
Download or read book Algorithms in C written by Robert Sedgewick and published by . This book was released on 2001 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Python Algorithms by : Magnus Lie Hetland
Download or read book Python Algorithms written by Magnus Lie Hetland and published by Apress. This book was released on 2014-09-17 with total page 303 pages. Available in PDF, EPUB and Kindle. Book excerpt: Python Algorithms, Second Edition explains the Python approach to algorithm analysis and design. Written by Magnus Lie Hetland, author of Beginning Python, this book is sharply focused on classical algorithms, but it also gives a solid understanding of fundamental algorithmic problem-solving techniques. The book deals with some of the most important and challenging areas of programming and computer science in a highly readable manner. It covers both algorithmic theory and programming practice, demonstrating how theory is reflected in real Python programs. Well-known algorithms and data structures that are built into the Python language are explained, and the user is shown how to implement and evaluate others.
Download or read book Mastering Corda written by Jamiel Sheikh and published by "O'Reilly Media, Inc.". This book was released on 2020-10-09 with total page 486 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mastering Corda provides you with a consistent, linear, and paced path to learning Corda and building modern enterprise-grade decentralized applications. Using this book, anyone from a complete blockchain beginner to an experienced blockchain or enterprise architect can rapidly understand and write applications like a pro while exploring the technical nuances and intricacies of the Corda platform. Corda is designed for use cases such as finance and investments, supply chain, healthcare, trade finance, insurance, and real estate that require a high-volume of transactions, scalability, and data privacy. If you have basic Java skills, this book will help you understand blockchain and show how you can get started immediately and be involved in the disruption of the future. With this book, you will: Understand Corda's value proposition and alignment with business strategies--particularly relevant to business executives and architects Dive deep into Corda's architecture and blockchain fundamentals Rapidly gain extensive knowledge of and hands-on experience with building Corda applications Compare and contrast Corda with Bitcoin, Ethereum, and Hyperledger Effectively prepare for the Corda certification exam and job interviews involving blockchain Perform data analytics and machine learning on Corda nodes
Book Synopsis Introduction to Algorithms, third edition by : Thomas H. Cormen
Download or read book Introduction to Algorithms, third edition written by Thomas H. Cormen and published by MIT Press. This book was released on 2009-07-31 with total page 1313 pages. Available in PDF, EPUB and Kindle. Book excerpt: The latest edition of the essential text and professional reference, with substantial new material on such topics as vEB trees, multithreaded algorithms, dynamic programming, and edge-based flow. Some books on algorithms are rigorous but incomplete; others cover masses of material but lack rigor. Introduction to Algorithms uniquely combines rigor and comprehensiveness. The book covers a broad range of algorithms in depth, yet makes their design and analysis accessible to all levels of readers. Each chapter is relatively self-contained and can be used as a unit of study. The algorithms are described in English and in a pseudocode designed to be readable by anyone who has done a little programming. The explanations have been kept elementary without sacrificing depth of coverage or mathematical rigor. The first edition became a widely used text in universities worldwide as well as the standard reference for professionals. The second edition featured new chapters on the role of algorithms, probabilistic analysis and randomized algorithms, and linear programming. The third edition has been revised and updated throughout. It includes two completely new chapters, on van Emde Boas trees and multithreaded algorithms, substantial additions to the chapter on recurrence (now called “Divide-and-Conquer”), and an appendix on matrices. It features improved treatment of dynamic programming and greedy algorithms and a new notion of edge-based flow in the material on flow networks. Many exercises and problems have been added for this edition. The international paperback edition is no longer available; the hardcover is available worldwide.
