Algorithms From The Book Second Edition
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Algorithms Of The Intelligent Web
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Author : Haralambos Marmanis
language : en
Publisher: Manning Publications
Release Date : 2009
Algorithms Of The Intelligent Web written by Haralambos Marmanis and has been published by Manning Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009 with Computers categories.
"Algorithms of the Intelligent Web" is an example-driven blueprint for creating applications that collect, analyze, and act on the massive quantities of data users leave in their wake as they use the Web. Readers learn to build Netflix-style recommendation engines, and how to apply the same techniques to social-networking sites.
Essential Algorithms
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Author : Rod Stephens
language : en
Publisher: John Wiley & Sons
Release Date : 2019-05-15
Essential Algorithms written by Rod Stephens and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-05-15 with Computers categories.
A friendly introduction to the most useful algorithms written in simple, intuitive English The revised and updated second edition of Essential Algorithms, offers an accessible introduction to computer algorithms. The book contains a description of important classical algorithms and explains when each is appropriate. The author shows how to analyze algorithms in order to understand their behavior and teaches techniques that the can be used to create new algorithms to meet future needs. The text includes useful algorithms such as: methods for manipulating common data structures, advanced data structures, network algorithms, and numerical algorithms. It also offers a variety of general problem-solving techniques. In addition to describing algorithms and approaches, the author offers details on how to analyze the performance of algorithms. The book is filled with exercises that can be used to explore ways to modify the algorithms in order to apply them to new situations. This updated edition of Essential Algorithms: Contains explanations of algorithms in simple terms, rather than complicated math Steps through powerful algorithms that can be used to solve difficult programming problems Helps prepare for programming job interviews that typically include algorithmic questions Offers methods can be applied to any programming language Includes exercises and solutions useful to both professionals and students Provides code examples updated and written in Python and C# Essential Algorithms has been updated and revised and offers professionals and students a hands-on guide to analyzing algorithms as well as the techniques and applications. The book also includes a collection of questions that may appear in a job interview. The book’s website will include reference implementations in Python and C# (which can be easily applied to Java and C++).
Algorithmic Thinking
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Author : Daniel Zingaro
language : en
Publisher: No Starch Press
Release Date : 2020-12-15
Algorithmic Thinking written by Daniel Zingaro and has been published by No Starch Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-12-15 with Computers categories.
A hands-on, problem-based introduction to building algorithms and data structures to solve problems with a computer. Algorithmic Thinking will teach you how to solve challenging programming problems and design your own algorithms. Daniel Zingaro, a master teacher, draws his examples from world-class programming competitions like USACO and IOI. You'll learn how to classify problems, choose data structures, and identify appropriate algorithms. You'll also learn how your choice of data structure, whether a hash table, heap, or tree, can affect runtime and speed up your algorithms; and how to adopt powerful strategies like recursion, dynamic programming, and binary search to solve challenging problems. Line-by-line breakdowns of the code will teach you how to use algorithms and data structures like: The breadth-first search algorithm to find the optimal way to play a board game or find the best way to translate a book Dijkstra's algorithm to determine how many mice can exit a maze or the number of fastest routes between two locations The union-find data structure to answer questions about connections in a social network or determine who are friends or enemies The heap data structure to determine the amount of money given away in a promotion The hash-table data structure to determine whether snowflakes are unique or identify compound words in a dictionary NOTE: Each problem in this book is available on a programming-judge website. You'll find the site's URL and problem ID in the description. What's better than a free correctness check?
Distributed Algorithms
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Author : Wan Fokkink
language : en
Publisher: MIT Press
Release Date : 2013-12-06
Distributed Algorithms written by Wan Fokkink and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-12-06 with Computers categories.
A comprehensive guide to distributed algorithms that emphasizes examples and exercises rather than mathematical argumentation.
Algorithms From The Book Second Edition
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Author : Kenneth Lange
language : en
Publisher: SIAM
Release Date : 2025-06-12
Algorithms From The Book Second Edition written by Kenneth Lange and has been published by SIAM this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-12 with Science categories.
Most books on algorithms are narrowly focused on a single field of application. This unique book cuts across discipline boundaries, exposing readers to the most successful algorithms from a variety of fields. Algorithm derivation is a legitimate branch of the mathematical sciences driven by hardware advances and the demands of many scientific fields. The best algorithms are undergirded by beautiful mathematics. This book enables readers to look under the hood and understand how some basic algorithms operate and how to assemble complex algorithms from simpler building blocks. Since publication of the first edition of Algorithms from THE BOOK, the number of new algorithms has swelled exponentially, with the fields of neural net modeling and natural language processing leading the way. These developments warranted the addition of a new chapter on automatic differentiation and its applications to neural net modeling. The second edition also corrects previous errors, clarifies explanations, adds worked exercises, and introduces new algorithms in existing chapters. In Algorithms from THE BOOK, Second Edition, the majority of algorithms are accompanied by Julia code for experimentation, the many classroom-tested exercises make the material suitable for use as a textbook, and appendices contain not only background material often missing in undergraduate education but also solutions to selected problems. This book is intended for students and professionals in the mathematical sciences, physical sciences, engineering, and the quantitative sectors of the biological and social sciences.
Data Science Algorithms In A Week
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Author : Dávid Natingga
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-10-31
Data Science Algorithms In A Week written by Dávid Natingga and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-10-31 with Computers categories.
Build a strong foundation of machine learning algorithms in 7 days Key FeaturesUse Python and its wide array of machine learning libraries to build predictive models Learn the basics of the 7 most widely used machine learning algorithms within a weekKnow when and where to apply data science algorithms using this guideBook Description Machine learning applications are highly automated and self-modifying, and continue to improve over time with minimal human intervention, as they learn from the trained data. To address the complex nature of various real-world data problems, specialized machine learning algorithms have been developed. Through algorithmic and statistical analysis, these models can be leveraged to gain new knowledge from existing data as well. Data Science Algorithms in a Week addresses all problems related to accurate and efficient data classification and prediction. Over the course of seven days, you will be introduced to seven algorithms, along with exercises that will help you understand different aspects of machine learning. You will see how to pre-cluster your data to optimize and classify it for large datasets. This book also guides you in predicting data based on existing trends in your dataset. This book covers algorithms such as k-nearest neighbors, Naive Bayes, decision trees, random forest, k-means, regression, and time-series analysis. By the end of this book, you will understand how to choose machine learning algorithms for clustering, classification, and regression and know which is best suited for your problem What you will learnUnderstand how to identify a data science problem correctlyImplement well-known machine learning algorithms efficiently using PythonClassify your datasets using Naive Bayes, decision trees, and random forest with accuracyDevise an appropriate prediction solution using regressionWork with time series data to identify relevant data events and trendsCluster your data using the k-means algorithmWho this book is for This book is for aspiring data science professionals who are familiar with Python and have a little background in statistics. You’ll also find this book useful if you’re currently working with data science algorithms in some capacity and want to expand your skill set
Algorithms In A Nutshell
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Author : George T. Heineman
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2008-10-14
Algorithms In A Nutshell written by George T. Heineman and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-10-14 with Computers categories.
Creating robust software requires the use of efficient algorithms, but programmers seldom think about them until a problem occurs. 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 With Algorithms in a Nutshell, you'll learn how to improve the performance of key algorithms essential for the success of your software applications.
Machine Learning Algorithms
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Author : Giuseppe Bonaccorso
language : en
Publisher: Packt Publishing Ltd
Release Date : 2017-07-24
Machine Learning Algorithms written by Giuseppe Bonaccorso and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-07-24 with Computers categories.
Build strong foundation for entering the world of Machine Learning and data science with the help of this comprehensive guide About This Book Get started in the field of Machine Learning with the help of this solid, concept-rich, yet highly practical guide. Your one-stop solution for everything that matters in mastering the whats and whys of Machine Learning algorithms and their implementation. Get a solid foundation for your entry into Machine Learning by strengthening your roots (algorithms) with this comprehensive guide. Who This Book Is For This book is for IT professionals who want to enter the field of data science and are very new to Machine Learning. Familiarity with languages such as R and Python will be invaluable here. What You Will Learn Acquaint yourself with important elements of Machine Learning Understand the feature selection and feature engineering process Assess performance and error trade-offs for Linear Regression Build a data model and understand how it works by using different types of algorithm Learn to tune the parameters of Support Vector machines Implement clusters to a dataset Explore the concept of Natural Processing Language and Recommendation Systems Create a ML architecture from scratch. In Detail As the amount of data continues to grow at an almost incomprehensible rate, being able to understand and process data is becoming a key differentiator for competitive organizations. Machine learning applications are everywhere, from self-driving cars, spam detection, document search, and trading strategies, to speech recognition. This makes machine learning well-suited to the present-day era of Big Data and Data Science. The main challenge is how to transform data into actionable knowledge. In this book you will learn all the important Machine Learning algorithms that are commonly used in the field of data science. These algorithms can be used for supervised as well as unsupervised learning, reinforcement learning, and semi-supervised learning. A few famous algorithms that are covered in this book are Linear regression, Logistic Regression, SVM, Naive Bayes, K-Means, Random Forest, TensorFlow, and Feature engineering. In this book you will also learn how these algorithms work and their practical implementation to resolve your problems. This book will also introduce you to the Natural Processing Language and Recommendation systems, which help you run multiple algorithms simultaneously. On completion of the book you will have mastered selecting Machine Learning algorithms for clustering, classification, or regression based on for your problem. Style and approach An easy-to-follow, step-by-step guide that will help you get to grips with real -world applications of Algorithms for Machine Learning.
Python Algorithms
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Author : Magnus Lie Hetland
language : en
Publisher: Apress
Release Date : 2014-09-17
Python Algorithms written by Magnus Lie Hetland and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-09-17 with Computers categories.
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.
Digraphs
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Author : Jorgen Bang-Jensen
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-06-29
Digraphs written by Jorgen Bang-Jensen and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-06-29 with Mathematics categories.
Graph theory is a very popular area of discrete mathematics with not only numerous theoretical developments, but also countless applications to prac tical problems. As a research area, graph theory is still relatively young, but it is maturing rapidly with many deep results having been discovered over the last couple of decades. The theory of graphs can be roughly partitioned into two branches: the areas of undirected graphs and directed graphs (digraphs). Even though both areas have numerous important applications, for various reasons, undirected graphs have been studied much more extensively than directed graphs. One of the reasons is that undirected graphs form in a sense a special class of directed graphs (symmetric digraphs) and hence problems that can be for mulated for both directed and undirected graphs are often easier for the latter. Another reason is that, unlike for the case of undirected graphs, for which there are several important books covering both classical and recent results, no previous book covers more than a small fraction of the results obtained on digraphs within the last 25 years. Typically, digraphs are consid ered only in one chapter or by a few elementary results scattered throughout the book. Despite all this, the theory of directed graphs has developed enormously within the last three decades. There is an extensive literature on digraphs (more than 3000 papers). Many of these papers contain, not only interesting theoretical results, but also important algorithms as well as applications.