Products related to Algorithms:
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Algorithms : Big Data, Optimization Techniques, Cyber Security
Algorithms are ubiquitous in the contemporary technological world, and they ultimately consist of finite sequences of instructions used to accomplish tasks with necessary input values.This book analyses the top performing algorithms in areas as diverse as Big Data, Artificial Intelligence, Optimization Techniques and Cloud & Cyber Security Systems in order to explore their power and limitations.
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Think Data Structures : Algorithms and Information Retrieval in Java
If you’re a student studying computer science or a software developer preparing for technical interviews, this practical book will help you learn and review some of the most important ideas in software engineering—data structures and algorithms—in a way that’s clearer, more concise, and more engaging than other materials.By emphasizing practical knowledge and skills over theory, author Allen Downey shows you how to use data structures to implement efficient algorithms, and then analyze and measure their performance.You’ll explore the important classes in the Java collections framework (JCF), how they’re implemented, and how they’re expected to perform.Each chapter presents hands-on exercises supported by test code online.Use data structures such as lists and maps, and understand how they work Build an application that reads Wikipedia pages, parses the contents, and navigates the resulting data tree Analyze code to predict how fast it will run and how much memory it will require Write classes that implement the Map interface, using a hash table and binary search tree Build a simple web search engine with a crawler, an indexer that stores web page contents, and a retriever that returns user query results Other books by Allen Downey include Think Java, Think Python, Think Stats, and Think Bayes.
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Information Disorder : Algorithms and Society
This book focuses on the recent rise of “infodemics” as forms of disinformation, misinformation and mal-information saturate contemporary media platforms, shaping public opinion to advance agendas. The internet in general and social media in particular have relativized, through their global, complex and instantaneous information flows, assumptions about truth and authority in fact-based content.This has created new opportunities for state actors to use information beyond traditional conceptions of propaganda to directly assault a public’s conception of reality.Additionally, almost anyone has the capability to challenge evidential claims through narratives and imagery alone as there is a wide appetite online for alternative realities.This requires new approaches to media literacy in education, the creative arts and our acts of media consumption and dissemination.The volume covers the ways that social media platforms amplify and catalyze the messages of politicians and influencers, the ambivalence of algorithms that can both generate and detect problematic information, how fake news imitates the style of memes to gain widespread social traction and virality, and how artists have intentionally created “sicko AIs” in new media performances to highlight the ethical risks of increasingly “intelligent” technologies. Scholars and students from many backgrounds, as well as policy makers, journalists and the general reading public, will find a multidisciplinary approach to questions posed by information disorder research from the fields of communication, social psychology, human-computer interaction, journalism, media, semiotics and new media art.
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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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What are algorithms and data structures for arrays?
Algorithms for arrays are step-by-step procedures or formulas for solving problems related to arrays, such as searching, sorting, and manipulating elements. Data structures for arrays refer to the way elements are stored and organized in memory, such as one-dimensional arrays, multi-dimensional arrays, and dynamic arrays. These algorithms and data structures are essential for efficient data manipulation and retrieval in programming and computer science.
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Are algorithms and data structures important or secondary?
Algorithms and data structures are crucial in computer science and programming. They are the foundation of efficient problem-solving and are essential for writing optimized and scalable code. Without a solid understanding of algorithms and data structures, it is difficult to write efficient and reliable software. Therefore, they are definitely important and not secondary in the field of computer science.
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How do relaxation algorithms and data structures work here?
Relaxation algorithms and data structures work together to efficiently find the shortest path in a graph. The relaxation algorithm iterates through the edges of the graph, updating the distance to each vertex if a shorter path is found. This process continues until no further updates are possible, ensuring that the shortest path to each vertex is found. Data structures such as priority queues are often used to efficiently store and retrieve the vertices with the smallest distance, allowing the relaxation algorithm to operate with optimal time complexity. Overall, relaxation algorithms and data structures work in tandem to efficiently find the shortest path in a graph.
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What is the runtime of algorithms and data structures?
The runtime of algorithms and data structures refers to the amount of time it takes for an algorithm to execute and produce a result. It is a measure of the efficiency of the algorithm in terms of time complexity. The runtime of algorithms and data structures can vary depending on the input size and the specific implementation of the algorithm. It is important to analyze and understand the runtime of algorithms and data structures in order to choose the most efficient solution for a given problem.
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Information Theory, Inference and Learning Algorithms
Information theory and inference, taught together in this exciting textbook, lie at the heart of many important areas of modern technology - communication, signal processing, data mining, machine learning, pattern recognition, computational neuroscience, bioinformatics and cryptography.The book introduces theory in tandem with applications.Information theory is taught alongside practical communication systems such as arithmetic coding for data compression and sparse-graph codes for error-correction.Inference techniques, including message-passing algorithms, Monte Carlo methods and variational approximations, are developed alongside applications to clustering, convolutional codes, independent component analysis, and neural networks.Uniquely, the book covers state-of-the-art error-correcting codes, including low-density-parity-check codes, turbo codes, and digital fountain codes - the twenty-first-century standards for satellite communications, disk drives, and data broadcast.Richly illustrated, filled with worked examples and over 400 exercises, some with detailed solutions, the book is ideal for self-learning, and for undergraduate or graduate courses.It also provides an unparalleled entry point for professionals in areas as diverse as computational biology, financial engineering and machine learning.
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Hybrid Encryption Algorithms over Wireless Communication Channels
This book presents novel hybrid encryption algorithms that possess many different characteristics.In particular, “Hybrid Encryption Algorithms over Wireless Communication Channels”, examines encrypted image and video data for the purpose of secure wireless communications.A study of two different families of encryption schemes are introduced: namely, permutation-based and diffusion-based schemes.The objective of the book is to help the reader selecting the best suited scheme for the transmission of encrypted images and videos over wireless communications channels, with the aid of encryption and decryption quality metrics.This is achieved by applying number-theory based encryption algorithms, such as chaotic theory with different modes of operations, the Advanced Encryption Standard (AES), and the RC6 in a pre-processing step in order to achieve the required permutation and diffusion.The Rubik’s cube is used afterwards in order to maximize the number of permutations. Transmission of images and videos is vital in today’s communications systems.Hence, an effective encryption and modulation schemes are a must.The author adopts Orthogonal Frequency Division Multiplexing (OFDM), as the multicarrier transmission choice for wideband communications.For completeness, the author addresses the sensitivity of the encrypted data to the wireless channel impairments, and the effect of channel equalization on the received images and videos quality. Complete simulation experiments with MATLAB® codes are included.The book will help the reader obtain the required understanding for selecting the suitable encryption method that best fulfills the application requirements.
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Data Structures and Algorithms
This book is an outcome of long years of teaching experience for undergraduate as well as post graduate students, and is an attempt to put together all the essential topics of data structures and Algorithms for easy reference.The under-graduate students of computer science and engineering, post-graduate students of computer applications and computer science and engineering will find this book very useful.It contains several multiple choice questions under each chapter which will be useful for those who aspire to write the GATE examination.A brief course on C-programming language is given to help the students in implementing the algorithms.Salient Features:The data structures are explained with simple relevant examples. Algorithms are written in simple pseudo-code and are easy to convert to any programming language. Concepts are well supported with suitable examples and diagrams for the students to find it easy to follow. Includes review questions as well as MCQs with answers. Critical examples with stepwise explanation makes it a great instructor resource.
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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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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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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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Are algorithms and data structures difficult to study in college?
Algorithms and data structures can be challenging to study in college, as they require a strong foundation in mathematics and computer science. Understanding the intricacies of algorithms and data structures often involves complex problem-solving and critical thinking skills. However, with dedication and practice, students can grasp these concepts and excel in their studies. Additionally, many colleges offer resources such as tutoring, study groups, and office hours to support students in their learning of algorithms and data structures.
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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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