Download or read book Understanding Complex Datasets written by David Skillicorn and published by CRC Press. This book was released on 2007-05-17 with total page 268 pages. Available in PDF, EPUB and Kindle. Book excerpt: Making obscure knowledge about matrix decompositions widely available, Understanding Complex Datasets: Data Mining with Matrix Decompositions discusses the most common matrix decompositions and shows how they can be used to analyze large datasets in a broad range of application areas. Without having to understand every mathematical detail, the book

Download or read book Mining of Massive Datasets written by Jure Leskovec and published by Cambridge University Press. This book was released on 2014-11-13 with total page 480 pages. Available in PDF, EPUB and Kindle. Book excerpt: Now in its second edition, this book focuses on practical algorithms for mining data from even the largest datasets.

Download or read book Algorithms and Data Structures for Massive Datasets written by Dzejla Medjedovic and published by Simon and Schuster. This book was released on 2022-08-16 with total page 302 pages. Available in PDF, EPUB and Kindle. Book excerpt: Massive modern datasets make traditional data structures and algorithms grind to a halt. This fun and practical guide introduces cutting-edge techniques that can reliably handle even the largest distributed datasets. In Algorithms and Data Structures for Massive Datasets you will learn: Probabilistic sketching data structures for practical problems Choosing the right database engine for your application Evaluating and designing efficient on-disk data structures and algorithms Understanding the algorithmic trade-offs involved in massive-scale systems Deriving basic statistics from streaming data Correctly sampling streaming data Computing percentiles with limited space resources Algorithms and Data Structures for Massive Datasets reveals a toolbox of new methods that are perfect for handling modern big data applications. You’ll explore the novel data structures and algorithms that underpin Google, Facebook, and other enterprise applications that work with truly massive amounts of data. These effective techniques can be applied to any discipline, from finance to text analysis. Graphics, illustrations, and hands-on industry examples make complex ideas practical to implement in your projects—and there’s no mathematical proofs to puzzle over. Work through this one-of-a-kind guide, and you’ll find the sweet spot of saving space without sacrificing your data’s accuracy. About the technology Standard algorithms and data structures may become slow—or fail altogether—when applied to large distributed datasets. Choosing algorithms designed for big data saves time, increases accuracy, and reduces processing cost. This unique book distills cutting-edge research papers into practical techniques for sketching, streaming, and organizing massive datasets on-disk and in the cloud. About the book Algorithms and Data Structures for Massive Datasets introduces processing and analytics techniques for large distributed data. Packed with industry stories and entertaining illustrations, this friendly guide makes even complex concepts easy to understand. You’ll explore real-world examples as you learn to map powerful algorithms like Bloom filters, Count-min sketch, HyperLogLog, and LSM-trees to your own use cases. What's inside Probabilistic sketching data structures Choosing the right database engine Designing efficient on-disk data structures and algorithms Algorithmic tradeoffs in massive-scale systems Computing percentiles with limited space resources About the reader Examples in Python, R, and pseudocode. About the author Dzejla Medjedovic earned her PhD in the Applied Algorithms Lab at Stony Brook University, New York. Emin Tahirovic earned his PhD in biostatistics from University of Pennsylvania. Illustrator Ines Dedovic earned her PhD at the Institute for Imaging and Computer Vision at RWTH Aachen University, Germany. Table of Contents 1 Introduction PART 1 HASH-BASED SKETCHES 2 Review of hash tables and modern hashing 3 Approximate membership: Bloom and quotient filters 4 Frequency estimation and count-min sketch 5 Cardinality estimation and HyperLogLog PART 2 REAL-TIME ANALYTICS 6 Streaming data: Bringing everything together 7 Sampling from data streams 8 Approximate quantiles on data streams PART 3 DATA STRUCTURES FOR DATABASES AND EXTERNAL MEMORY ALGORITHMS 9 Introducing the external memory model 10 Data structures for databases: B-trees, Bε-trees, and LSM-trees 11 External memory sorting

Download or read book Data Mining Concepts and Techniques written by Jiawei Han and published by Elsevier. This book was released on 2011-06-09 with total page 740 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data Mining: Concepts and Techniques provides the concepts and techniques in processing gathered data or information, which will be used in various applications. Specifically, it explains data mining and the tools used in discovering knowledge from the collected data. This book is referred as the knowledge discovery from data (KDD). It focuses on the feasibility, usefulness, effectiveness, and scalability of techniques of large data sets. After describing data mining, this edition explains the methods of knowing, preprocessing, processing, and warehousing data. It then presents information about data warehouses, online analytical processing (OLAP), and data cube technology. Then, the methods involved in mining frequent patterns, associations, and correlations for large data sets are described. The book details the methods for data classification and introduces the concepts and methods for data clustering. The remaining chapters discuss the outlier detection and the trends, applications, and research frontiers in data mining. This book is intended for Computer Science students, application developers, business professionals, and researchers who seek information on data mining. Presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects Addresses advanced topics such as mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in several fields Provides a comprehensive, practical look at the concepts and techniques you need to get the most out of your data

Download or read book Big and Complex Data Analysis written by S. Ejaz Ahmed and published by Springer. This book was released on 2017-03-21 with total page 386 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume conveys some of the surprises, puzzles and success stories in high-dimensional and complex data analysis and related fields. Its peer-reviewed contributions showcase recent advances in variable selection, estimation and prediction strategies for a host of useful models, as well as essential new developments in the field. The continued and rapid advancement of modern technology now allows scientists to collect data of increasingly unprecedented size and complexity. Examples include epigenomic data, genomic data, proteomic data, high-resolution image data, high-frequency financial data, functional and longitudinal data, and network data. Simultaneous variable selection and estimation is one of the key statistical problems involved in analyzing such big and complex data. The purpose of this book is to stimulate research and foster interaction between researchers in the area of high-dimensional data analysis. More concretely, its goals are to: 1) highlight and expand the breadth of existing methods in big data and high-dimensional data analysis and their potential for the advancement of both the mathematical and statistical sciences; 2) identify important directions for future research in the theory of regularization methods, in algorithmic development, and in methodologies for different application areas; and 3) facilitate collaboration between theoretical and subject-specific researchers.

Download or read book Using Secondary Datasets to Understand Persons with Developmental Disabilities and their Families written by and published by Academic Press. This book was released on 2013-10-15 with total page 388 pages. Available in PDF, EPUB and Kindle. Book excerpt: International Review of Research in Developmental Disabilities is an ongoing scholarly look at research into the causes, effects, classification systems, syndromes, etc. of developmental disabilities. Contributors come from wide-ranging perspectives, including genetics, psychology, education, and other health and behavioral sciences. Provides the most recent scholarly research in the study of developmental disabilities A vast range of perspectives is offered, and many topics are covered An excellent resource for academic researchers

Download or read book R for Data Science written by Hadley Wickham and published by "O'Reilly Media, Inc.". This book was released on 2016-12-12 with total page 521 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true "signals" in your dataset Communicate—learn R Markdown for integrating prose, code, and results

Download or read book Introduction to Data Science written by Rafael A. Irizarry and published by CRC Press. This book was released on 2019-11-20 with total page 794 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduction to Data Science: Data Analysis and Prediction Algorithms with R introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression, and machine learning. It also helps you develop skills such as R programming, data wrangling, data visualization, predictive algorithm building, file organization with UNIX/Linux shell, version control with Git and GitHub, and reproducible document preparation. This book is a textbook for a first course in data science. No previous knowledge of R is necessary, although some experience with programming may be helpful. The book is divided into six parts: R, data visualization, statistics with R, data wrangling, machine learning, and productivity tools. Each part has several chapters meant to be presented as one lecture. The author uses motivating case studies that realistically mimic a data scientist’s experience. He starts by asking specific questions and answers these through data analysis so concepts are learned as a means to answering the questions. Examples of the case studies included are: US murder rates by state, self-reported student heights, trends in world health and economics, the impact of vaccines on infectious disease rates, the financial crisis of 2007-2008, election forecasting, building a baseball team, image processing of hand-written digits, and movie recommendation systems. The statistical concepts used to answer the case study questions are only briefly introduced, so complementing with a probability and statistics textbook is highly recommended for in-depth understanding of these concepts. If you read and understand the chapters and complete the exercises, you will be prepared to learn the more advanced concepts and skills needed to become an expert.

Download or read book Complex Datasets and Inverse Problems written by Regina Y. Liu and published by IMS. This book was released on 2007 with total page 286 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Understanding Machine Learning written by Shai Shalev-Shwartz and published by Cambridge University Press. This book was released on 2014-05-19 with total page 415 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.

Download or read book Learning from Complex Datasets written by Geoffrey J. McLachlan and published by Wiley-Blackwell. This book was released on 2012-02-24 with total page 416 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides insight and advice on the most appropriate and effective statistical methods to employ when using large or robust data. It covers the handling of high-dimensional data and data in which there is bias in the type collected and presents applications in modern and molecular genetics to showcase the most challenging datasets. In addition, it features full-color art throughout the book to illustrate the importance of color in data understanding and interpretation and offers access to a dedicated author web site.

Download or read book Data Science and Analytics with Python written by Jesus Rogel-Salazar and published by CRC Press. This book was released on 2018-02-05 with total page 345 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data Science and Analytics with Python is designed for practitioners in data science and data analytics in both academic and business environments. The aim is to present the reader with the main concepts used in data science using tools developed in Python, such as SciKit-learn, Pandas, Numpy, and others. The use of Python is of particular interest, given its recent popularity in the data science community. The book can be used by seasoned programmers and newcomers alike. The book is organized in a way that individual chapters are sufficiently independent from each other so that the reader is comfortable using the contents as a reference. The book discusses what data science and analytics are, from the point of view of the process and results obtained. Important features of Python are also covered, including a Python primer. The basic elements of machine learning, pattern recognition, and artificial intelligence that underpin the algorithms and implementations used in the rest of the book also appear in the first part of the book. Regression analysis using Python, clustering techniques, and classification algorithms are covered in the second part of the book. Hierarchical clustering, decision trees, and ensemble techniques are also explored, along with dimensionality reduction techniques and recommendation systems. The support vector machine algorithm and the Kernel trick are discussed in the last part of the book. About the Author Dr. Jesús Rogel-Salazar is a Lead Data scientist with experience in the field working for companies such as AKQA, IBM Data Science Studio, Dow Jones and others. He is a visiting researcher at the Department of Physics at Imperial College London, UK and a member of the School of Physics, Astronomy and Mathematics at the University of Hertfordshire, UK, He obtained his doctorate in physics at Imperial College London for work on quantum atom optics and ultra-cold matter. He has held a position as senior lecturer in mathematics as well as a consultant in the financial industry since 2006. He is the author of the book Essential Matlab and Octave, also published by CRC Press. His interests include mathematical modelling, data science, and optimization in a wide range of applications including optics, quantum mechanics, data journalism, and finance.

Download or read book Hacking The Art of Exploitation 2nd Edition written by Jon Erickson and published by No Starch Press. This book was released on 2008-02-01 with total page 492 pages. Available in PDF, EPUB and Kindle. Book excerpt: Hacking is the art of creative problem solving, whether that means finding an unconventional solution to a difficult problem or exploiting holes in sloppy programming. Many people call themselves hackers, but few have the strong technical foundation needed to really push the envelope. Rather than merely showing how to run existing exploits, author Jon Erickson explains how arcane hacking techniques actually work. To share the art and science of hacking in a way that is accessible to everyone, Hacking: The Art of Exploitation, 2nd Edition introduces the fundamentals of C programming from a hacker's perspective. The included LiveCD provides a complete Linux programming and debugging environment—all without modifying your current operating system. Use it to follow along with the book's examples as you fill gaps in your knowledge and explore hacking techniques on your own. Get your hands dirty debugging code, overflowing buffers, hijacking network communications, bypassing protections, exploiting cryptographic weaknesses, and perhaps even inventing new exploits. This book will teach you how to: – Program computers using C, assembly language, and shell scripts – Corrupt system memory to run arbitrary code using buffer overflows and format strings – Inspect processor registers and system memory with a debugger to gain a real understanding of what is happening – Outsmart common security measures like nonexecutable stacks and intrusion detection systems – Gain access to a remote server using port-binding or connect-back shellcode, and alter a server's logging behavior to hide your presence – Redirect network traffic, conceal open ports, and hijack TCP connections – Crack encrypted wireless traffic using the FMS attack, and speed up brute-force attacks using a password probability matrix Hackers are always pushing the boundaries, investigating the unknown, and evolving their art. Even if you don't already know how to program, Hacking: The Art of Exploitation, 2nd Edition will give you a complete picture of programming, machine architecture, network communications, and existing hacking techniques. Combine this knowledge with the included Linux environment, and all you need is your own creativity.

Download or read book Handbook of Statistical Analysis and Data Mining Applications written by Robert Nisbet and published by Elsevier. This book was released on 2017-11-09 with total page 822 pages. Available in PDF, EPUB and Kindle. Book excerpt: Handbook of Statistical Analysis and Data Mining Applications, Second Edition, is a comprehensive professional reference book that guides business analysts, scientists, engineers and researchers, both academic and industrial, through all stages of data analysis, model building and implementation. The handbook helps users discern technical and business problems, understand the strengths and weaknesses of modern data mining algorithms and employ the right statistical methods for practical application. This book is an ideal reference for users who want to address massive and complex datasets with novel statistical approaches and be able to objectively evaluate analyses and solutions. It has clear, intuitive explanations of the principles and tools for solving problems using modern analytic techniques and discusses their application to real problems in ways accessible and beneficial to practitioners across several areas—from science and engineering, to medicine, academia and commerce. Includes input by practitioners for practitioners Includes tutorials in numerous fields of study that provide step-by-step instruction on how to use supplied tools to build models Contains practical advice from successful real-world implementations Brings together, in a single resource, all the information a beginner needs to understand the tools and issues in data mining to build successful data mining solutions Features clear, intuitive explanations of novel analytical tools and techniques, and their practical applications

Download or read book Proceedings of Data Analytics and Management written by Abhishek Swaroop and published by Springer Nature. This book was released on 2024-02-06 with total page 666 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book includes original unpublished contributions presented at the International Conference on Data Analytics and Management (ICDAM 2023), held at London Metropolitan University, London, UK, during June 2023. The book covers the topics in data analytics, data management, big data, computational intelligence, and communication networks. The book presents innovative work by leading academics, researchers, and experts from industry which is useful for young researchers and students. The book is divided into four volumes.

Download or read book Large Scale Machine Learning in the Earth Sciences written by Ashok N. Srivastava and published by CRC Press. This book was released on 2017-08-01 with total page 354 pages. Available in PDF, EPUB and Kindle. Book excerpt: From the Foreword: "While large-scale machine learning and data mining have greatly impacted a range of commercial applications, their use in the field of Earth sciences is still in the early stages. This book, edited by Ashok Srivastava, Ramakrishna Nemani, and Karsten Steinhaeuser, serves as an outstanding resource for anyone interested in the opportunities and challenges for the machine learning community in analyzing these data sets to answer questions of urgent societal interest...I hope that this book will inspire more computer scientists to focus on environmental applications, and Earth scientists to seek collaborations with researchers in machine learning and data mining to advance the frontiers in Earth sciences." --Vipin Kumar, University of Minnesota Large-Scale Machine Learning in the Earth Sciences provides researchers and practitioners with a broad overview of some of the key challenges in the intersection of Earth science, computer science, statistics, and related fields. It explores a wide range of topics and provides a compilation of recent research in the application of machine learning in the field of Earth Science. Making predictions based on observational data is a theme of the book, and the book includes chapters on the use of network science to understand and discover teleconnections in extreme climate and weather events, as well as using structured estimation in high dimensions. The use of ensemble machine learning models to combine predictions of global climate models using information from spatial and temporal patterns is also explored. The second part of the book features a discussion on statistical downscaling in climate with state-of-the-art scalable machine learning, as well as an overview of methods to understand and predict the proliferation of biological species due to changes in environmental conditions. The problem of using large-scale machine learning to study the formation of tornadoes is also explored in depth. The last part of the book covers the use of deep learning algorithms to classify images that have very high resolution, as well as the unmixing of spectral signals in remote sensing images of land cover. The authors also apply long-tail distributions to geoscience resources, in the final chapter of the book.

Download or read book Simplified Machine Learning written by Dr. Pooja Sharma and published by BPB Publications. This book was released on 2024-06-15 with total page 328 pages. Available in PDF, EPUB and Kindle. Book excerpt: Explore the world of Artificial Intelligence with a deep understanding of Machine Learning concepts and algorithms KEY FEATURES ● A detailed study of mathematical concepts, Machine Learning concepts, and techniques. ● Discusses methods for evaluating model performances and interpreting results. ● Explores all types of Machine Learning (supervised, unsupervised, reinforcement, association rule mining, artificial neural network) in detail. ● Comprises numerous review questions and programming exercises at the end of every chapter. DESCRIPTION "Simplified Machine Learning" is a comprehensive guide that navigates readers through the intricate landscape of Machine Learning, offering a balanced blend of theory, algorithms, and practical applications. The first section introduces foundational concepts such as supervised and unsupervised learning, regression, classification, clustering, and feature engineering, providing a solid base in Machine Learning theory. The second section explores algorithms like decision trees, support vector machines, and neural networks, explaining their functions, strengths, and limitations, with a special focus on deep learning, reinforcement learning, and ensemble methods. The book also covers essential topics like model evaluation, hyperparameter tuning, and model interpretability. The final section transitions from theory to practice, equipping readers with hands-on experience in deploying models, building scalable systems, and understanding ethical considerations. By the end, readers will be able to leverage Machine Learning effectively in their respective fields, armed with practical skills and a strategic approach to problem-solving. WHAT YOU WILL LEARN ● Solid foundation in Machine Learning principles, algorithms, and methodologies. ● Implementation of Machine Learning models using popular libraries like NumPy, Pandas, PyTorch, or scikit-learn. ● Knowledge about selecting appropriate models, evaluating their performance, and tuning hyperparameters. ● Techniques to pre-process and engineer features for Machine Learning models. ● To frame real-world problems as Machine Learning tasks and apply appropriate techniques to solve them. WHO THIS BOOK IS FOR This book is designed for a diverse audience interested in Machine Learning, a core branch of Artificial Intelligence. Its intellectual coverage will benefit students, programmers, researchers, educators, AI enthusiasts, software engineers, and data scientists. TABLE OF CONTENTS 1. Introduction to Machine Learning 2. Data Pre-processing 3. Supervised Learning: Regression 4. Supervised Learning: Classification 5. Unsupervised Learning: Clustering 6. Dimensionality Reduction and Feature Selection 7. Association Rule Mining 8. Artificial Neural Network 9. Reinforcement Learning 10. Project Appendix Bibliography