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Understanding Complex Datasets by David Skillicorn Pdf
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
Using Secondary Datasets to Understand Persons with Developmental Disabilities and their Families by Anonim Pdf
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
Modelling the Physiological Human by Nadia Magnenat-Thalmann Pdf
This book constitutes the proceedings of the Second 3D Physiological Human Workshop, 3DPH 2009, held in Zermatt, Switzerland, in November/December 2009. The 19 revised full papers presented were carefully reviewed and selected from numerous submissions. The papers are organized in topical sections on Segmentation, Anatomical and Physiological Modelling, Simulation Models, Motion Analysis, Medical Visualization and Interaction, as well as Medical Ontology.
Hacking: The Art of Exploitation, 2nd Edition by Jon Erickson Pdf
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.
Large-Scale Machine Learning in the Earth Sciences by Ashok N. Srivastava,Ramakrishna Nemani,Karsten Steinhaeuser Pdf
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.
Data Science and Analytics with Python by Jesus Rogel-Salazar Pdf
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.
Simplified Machine Learning by Dr. Pooja Sharma Pdf
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
Proceedings of Data Analytics and Management by Abhishek Swaroop,Zdzislaw Polkowski,Sérgio Duarte Correia,Bal Virdee Pdf
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.
Learning from Complex Datasets by Geoffrey J. McLachlan,Shu-Kay A. Ng,Ian A. Wood Pdf
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.
Machine Learning and AI for Healthcare by Arjun Panesar Pdf
Explore the theory and practical applications of artificial intelligence (AI) and machine learning in healthcare. This book offers a guided tour of machine learning algorithms, architecture design, and applications of learning in healthcare and big data challenges. You’ll discover the ethical implications of healthcare data analytics and the future of AI in population and patient health optimization. You’ll also create a machine learning model, evaluate performance and operationalize its outcomes within your organization. Machine Learning and AI for Healthcare provides techniques on how to apply machine learning within your organization and evaluate the efficacy, suitability, and efficiency of AI applications. These are illustrated through leading case studies, including how chronic disease is being redefined through patient-led data learning and the Internet of Things. What You'll LearnGain a deeper understanding of key machine learning algorithms and their use and implementation within wider healthcare Implement machine learning systems, such as speech recognition and enhanced deep learning/AI Select learning methods/algorithms and tuning for use in healthcare Recognize and prepare for the future of artificial intelligence in healthcare through best practices, feedback loops and intelligent agentsWho This Book Is For Health care professionals interested in how machine learning can be used to develop health intelligence – with the aim of improving patient health, population health and facilitating significant care-payer cost savings.
Semantic Technology by Hideaki Takeda,Yuzhong Qu,Riichiro Mizoguchi,Yoshinobu Kitamura Pdf
This book constitutes the proceedings of the Second Joint International Semantic Technology Conference, JIST 2012, held in Nara, Japan, in December 2012. The 20 full papers and 13 short papers included in this volume were carefully reviewed and selected from 90 submissions. The regular papers deal with ontology and description logics; RDF and SPARQL; learning and discovery; semantic search; knowledge building; semantic Web application. The in-use track papers cover topics on social semantic Web and semantic search; and the special track papers have linked data in practice and database integration as a topic.
Algorithms and Data Structures for Massive Datasets by Dzejla Medjedovic,Emin Tahirovic Pdf
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
