Products related to Algorithms:
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Algorithms : Technology, Culture, Politics
Algorithms: Technology, Culture, Politics develops a relational, situated approach to algorithms.It takes a middle ground between theories that give the algorithm a singular and stable meaning in using it as a central analytic category for contemporary society and theories that dissolve the term into the details of empirical studies. The book discusses algorithms in relation to hardware and material conditions, code, data, and subjects such as users, programmers, but also “data doubles”.The individual chapters bridge critical discussions on bias, exclusion, or responsibility with the necessary detail on the contemporary state of information technology.The examples include state-of-the-art applications of machine learning, such as self-driving cars, and large language models such as GPT. The book will be of interest for everyone engaging critically with algorithms, particularly in the social sciences, media studies, STS, political theory, or philosophy.With its broad scope it can serve as a high-level introduction that picks up and builds on more than two decades of critical research on algorithms.
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Algorithms for Data Science
This textbook on practical data analytics unites fundamental principles, algorithms, and data.Algorithms are the keystone of data analytics and the focal point of this textbook.Clear and intuitive explanations of the mathematical and statistical foundations make the algorithms transparent.But practical data analytics requires more than just the foundations.Problems and data are enormously variable and only the most elementary of algorithms can be used without modification.Programming fluency and experience with real and challenging data is indispensable and so the reader is immersed in Python and R and real data analysis.By the end of the book, the reader will have gained the ability to adapt algorithms to new problems and carry out innovative analyses. This book has three parts:(a) Data Reduction: Begins with the concepts of data reduction, data maps, and information extraction.The second chapter introduces associative statistics, themathematical foundation of scalable algorithms and distributed computing.Practical aspects of distributed computing is the subject of the Hadoop and MapReduce chapter.(b) Extracting Information from Data: Linear regression and data visualization are the principal topics of Part II.The authors dedicate a chapter to the critical domain of Healthcare Analytics for an extended example of practical data analytics.The algorithms and analytics will be of much interest to practitioners interested in utilizing the large and unwieldly data sets of the Centers for Disease Control and Prevention's Behavioral Risk Factor Surveillance System.(c) Predictive Analytics Two foundational and widely used algorithms, k-nearest neighbors and naive Bayes, are developed in detail.A chapter is dedicated to forecasting. The last chapter focuses on streaming data and uses publicly accessible data streams originating from the Twitter API and the NASDAQ stock market in the tutorials. This book is intended for a one- or two-semester course in data analytics for upper-division undergraduate and graduate students in mathematics, statistics, and computer science.The prerequisites are kept low, and students with one or two courses in probability or statistics, an exposure to vectors and matrices, and a programming course will have no difficulty.The core material of every chapter is accessible to all with these prerequisites.The chapters often expand at the close with innovations of interest to practitioners of data science.Each chapter includes exercises of varying levels of difficulty.The text is eminently suitable for self-study and an exceptional resource for practitioners.
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Graph Algorithms for Data Science
Graphs are the natural way to understand connected data.This book explores the most important algorithms and techniques for graphs in data science, with practical examples and concrete advice on implementation and deployment. In Graph Algorithms for Data Science you will learn: Labeled-property graph modelingConstructing a graph from structured data such as CSV or SQLNLP techniques to construct a graph from unstructured dataCypher query language syntax to manipulate data and extract insightsSocial network analysis algorithms like PageRank and community detectionHow to translate graph structure to a ML model input with node embedding modelsUsing graph features in node classification and link prediction workflows Graph Algorithms for Data Science is a hands-on guide to working with graph-based data in applications like machine learning, fraud detection, and business data analysis.It's filled with fascinating and fun projects, demonstrating the ins-and-outs of graphs.You'll gain practical skills by analyzing Twitter, building graphs with NLP techniques, and much more.You don't need any graph experience to start benefiting from this insightful guide.These powerful graph algorithms are explained in clear, jargon-free text and illustrations that makes them easy to apply to your own projects. about the technology Graphs reveal the relationships in your data.Tracking these interlinking connections reveals new insights and influences and lets you analyze each data point as part of a larger whole.This interconnected data is perfect for machine learning, as well as analyzing social networks, communities, and even product recommendations. about the book Graph Algorithms for Data Science teaches you how to construct graphs from both structured and unstructured data.You'll learn how the flexible Cypher query language can be used to easily manipulate graph structures, and extract amazing insights.The book explores common and useful graph algorithms like PageRank and community detection/clustering algorithms.Each new algorithm you learn is instantly put into action to complete a hands-on data project, including modeling a social network!Finally, you'll learn how to utilize graphs to upgrade your machine learning, including utilizing node embedding models and graph neural networks.
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Automating the News : How Algorithms Are Rewriting the Media
From hidden connections in big data to bots spreading fake news, journalism is increasingly computer-generated.An expert in computer science and media explains the present and future of a world in which news is created by algorithm. Amid the push for self-driving cars and the roboticization of industrial economies, automation has proven one of the biggest news stories of our time.Yet the wide-scale automation of the news itself has largely escaped attention.In this lively exposé of that rapidly shifting terrain, Nicholas Diakopoulos focuses on the people who tell the stories—increasingly with the help of computer algorithms that are fundamentally changing the creation, dissemination, and reception of the news. Diakopoulos reveals how machine learning and data mining have transformed investigative journalism.Newsbots converse with social media audiences, distributing stories and receiving feedback.Online media has become a platform for A/B testing of content, helping journalists to better understand what moves audiences.Algorithms can even draft certain kinds of stories. These techniques enable media organizations to take advantage of experiments and economies of scale, enhancing the sustainability of the fourth estate.But they also place pressure on editorial decision-making, because they allow journalists to produce more stories, sometimes better ones, but rarely both. Automating the News responds to hype and fears surrounding journalistic algorithms by exploring the human influence embedded in automation.Though the effects of automation are deep, Diakopoulos shows that journalists are at little risk of being displaced.With algorithms at their fingertips, they may work differently and tell different stories than they otherwise would, but their values remain the driving force behind the news.The human–algorithm hybrid thus emerges as the latest embodiment of an age-old tension between commercial imperatives and journalistic principles.
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What are algorithms in computer science?
Algorithms in computer science are step-by-step procedures or formulas for solving a problem or accomplishing a task. They are a set of rules or instructions that are followed to achieve a particular goal. Algorithms are used in various computer science applications, such as sorting data, searching for information, and performing calculations. They are essential in programming and software development as they provide a systematic way to solve problems and process data efficiently.
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How do sorting algorithms work in computer science?
Sorting algorithms in computer science work by rearranging a collection of items into a specified order. They achieve this by comparing elements and swapping them based on a specific criteria, such as numerical value or alphabetical order. There are various sorting algorithms, each with its own approach and efficiency, such as bubble sort, merge sort, quick sort, and insertion sort. The choice of sorting algorithm depends on the size of the data set, the nature of the data, and the desired time and space complexity.
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For which problems can algorithms be used in computer science and for which problems can algorithms not be used?
Algorithms can be used in computer science to solve a wide range of problems, including sorting data, searching for specific items, optimizing routes, and processing large amounts of information. However, there are certain problems for which algorithms may not be suitable, such as those that are inherently unpredictable or require human intuition and creativity. Additionally, algorithms may not be effective for problems with extremely large or complex datasets that exceed the capabilities of current computing technology.
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What do algorithms achieve?
Algorithms achieve the ability to process and analyze large amounts of data quickly and efficiently. They help in making predictions, identifying patterns, and solving complex problems. Algorithms are used in various fields such as finance, healthcare, and technology to optimize processes and improve decision-making. Overall, algorithms play a crucial role in automating tasks, improving productivity, and driving innovation.
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Algorithms
Use your big monkey brain to do things that even your teachers can't do.With these books, you will talk to computers, create games, draw pictures and find information.Come on, code monkeys - let's write some code!
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Algorithms
Use your big monkey brain to do things that even your teachers can't do.With these books, you will talk to computers, create games, draw pictures and find information.Come on, code monkeys - let's write some code!
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Algorithms
An accessible introduction to algorithms, explaining not just what they are but how they work, with examples from a wide range of application areas.Digital technology runs on algorithms, sets of instructions that describe how to do something efficiently.Application areas range from search engines to tournament scheduling, DNA sequencing, and machine learning.Arguing that every educated person today needs to have some understanding of algorithms and what they do, in this volume in the MIT Press Essential Knowledge series, Panos Louridas offers an introduction to algorithms that is accessible to the nonspecialist reader.Louridas explains not just what algorithms are but also how they work, offering a wide range of examples and keeping mathematics to a minimum. After discussing what an algorithm does and how its effectiveness can be measured, Louridas covers three of the most fundamental applications areas: graphs, which describe networks, from eighteenth-century problems to today's social networks; searching, and how to find the fastest way to search; and sorting, and the importance of choosing the best algorithm for particular tasks.He then presents larger-scale applications: PageRank, Google's founding algorithm; and neural networks and deep learning.Finally, Louridas describes how all algorithms are nothing more than simple moves with pen and paper, and how from such a humble foundation rise all their spectacular achievements.
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Simple Algorithms
First Coding introduces children to the basic principles of programming and computing, from being safe on the internet to their first steps in computer logic.This series uses easy-to-read text and colourful images, as well as real examples of code.
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What do algorithms calculate?
Algorithms are designed to calculate specific tasks or operations based on a set of instructions. They can be used to perform mathematical calculations, process data, analyze patterns, make decisions, and solve problems. In essence, algorithms are used to automate and streamline various processes by following a predefined sequence of steps to produce a desired outcome.
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What are the Instagram algorithms?
The Instagram algorithms are a set of complex calculations used by the platform to determine what content users see on their feed. These algorithms analyze user behavior, such as likes, comments, and shares, to prioritize content from accounts that users engage with the most. The algorithms also take into account the timeliness of posts, the relationship between users, and the type of content being shared. By using these algorithms, Instagram aims to show users the most relevant and engaging content on their feed.
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Which sorting algorithms are there?
There are several common sorting algorithms, including bubble sort, selection sort, insertion sort, merge sort, quick sort, and heap sort. Each algorithm has its own advantages and disadvantages in terms of time complexity, space complexity, and stability. The choice of sorting algorithm depends on the specific requirements of the problem at hand.
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Should one learn without algorithms?
Learning without algorithms is certainly possible, as there are many different ways to acquire knowledge and skills. However, algorithms can be valuable tools for organizing and processing information, so learning about them can be beneficial. Understanding algorithms can help individuals solve complex problems, improve decision-making processes, and enhance their overall problem-solving abilities. Therefore, while it is not necessary to learn algorithms, doing so can certainly be advantageous in many fields.
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