A Computational Approach to Statistical Learning gives a novel introduction to predictive modeling by focusing on the algorithmic and numeric motivations behind popular statistical methods. The text contains annotated code to over 80 original reference functions. These functions provide minimal working implementations of common statistical learning algorithms. Every chapter concludes with a fully worked out application that illustrates predictive modeling tasks using a real-world dataset. The text begins with a detailed analysis of linear models and ordinary least squares. Subsequent chapters explore extensions such as ridge regression, generalized linear models, and additive models. The second half focuses on the use of general-purpose algorithms for convex optimization and their application to tasks in statistical learning. Models covered include the elastic net, dense neural networks, convolutional neural networks (CNNs), and spectral clustering. A unifying theme throughout the text is the use of optimization theory in the description of predictive models, with a particular focus on the singular value decomposition (SVD). Through this theme, the computational approach motivates and clarifies the relationships between various predictive models. Taylor Arnold is an assistant professor of statistics at the University of Richmond. His work at the intersection of computer vision, natural language processing, and digital humanities has been supported by multiple grants from the National Endowment for the Humanities (NEH) and the American Council of Learned Societies (ACLS). His first book, Humanities Data in R, was published in 2015. Michael Kane is an assistant professor of biostatistics at Yale University. He is the recipient of grants from the National Institutes of Health (NIH), DARPA, and the Bill and Melinda Gates Foundation. His R package bigmemory won the Chamber's prize for statistical software in 2010. Bryan Lewis is an applied mathematician and author of many popular R packages, including irlba, doRedis, and threejs.

A Computational Approach to Statistical Learning gives a novel introduction to predictive modeling by focusing on the algorithmic and numeric motivations behind popular statistical methods. The text contains annotated code to over 80 original reference functions. These functions provide minimal working implementations of common statistical learning algorithms. Every chapter concludes with a fully worked out application that illustrates predictive modeling tasks using a real-world dataset. The text begins with a detailed analysis of linear models and ordinary least squares. Subsequent chapters explore extensions such as ridge regression, generalized linear models, and additive models. The second half focuses on the use of general-purpose algorithms for convex optimization and their application to tasks in statistical learning. Models covered include the elastic net, dense neural networks, convolutional neural networks (CNNs), and spectral clustering. A unifying theme throughout the text is the use of optimization theory in the description of predictive models, with a particular focus on the singular value decomposition (SVD). Through this theme, the computational approach motivates and clarifies the relationships between various predictive models. Taylor Arnold is an assistant professor of statistics at the University of Richmond. His work at the intersection of computer vision, natural language processing, and digital humanities has been supported by multiple grants from the National Endowment for the Humanities (NEH) and the American Council of Learned Societies (ACLS). His first book, Humanities Data in R, was published in 2015. Michael Kane is an assistant professor of biostatistics at Yale University. He is the recipient of grants from the National Institutes of Health (NIH), DARPA, and the Bill and Melinda Gates Foundation. His R package bigmemory won the Chamber's prize for statistical software in 2010. Bryan Lewis is an applied mathematician and author of many popular R packages, including irlba, doRedis, and threejs.

Handbook of Big Data provides a state-of-the-art overview of the analysis of large-scale datasets. Featuring contributions from well-known experts in statistics and computer science, this handbook presents a carefully curated collection of techniques from both industry and academia. Thus, the text instills a working understanding of key statistical and computing ideas that can be readily applied in research and practice. Offering balanced coverage of methodology, theory, and applications, this handbook: Describes modern, scalable approaches for analyzing increasingly large datasets Defines the underlying concepts of the available analytical tools and techniques Details intercommunity advances in computational statistics and machine learning Handbook of Big Data also identifies areas in need of further development, encouraging greater communication and collaboration between researchers in big data sub-specialties such as genomics, computational biology, and finance.

Postmodern Time and Space in Fiction and Theory seeks to place the contemporary transformation of notions of space and time, often attributed to the technologies we use, in the context of the ongoing transformations of modernity. Bringing together examples of modern and contemporary fiction (from Defoe to DeLillo, Frankenstein to Finnegans Wake) and theoretical discussions of the modern and the post-modern, the author explores the legacy of modern transformations of space and time under five headings: "The Space of Nature"; "The Space of the City"; "Postmodern or Most Modern Time"; "The Time and Space of the Work of Art in the Age of Digital Reproduction"; and "Travel: from Modernity to...?". These five essays re-examine the meanings of modernity and its aftermath in relation to the spaces and times of the natural, the urban and the media environment.

Handbook of Big Data provides a state-of-the-art overview of the analysis of large-scale datasets. Featuring contributions from well-known experts in statistics and computer science, this handbook presents a carefully curated collection of techniques from both industry and academia. Thus, the text instills a working understanding of key statistical and computing ideas that can be readily applied in research and practice. Offering balanced coverage of methodology, theory, and applications, this handbook: Describes modern, scalable approaches for analyzing increasingly large datasets Defines the underlying concepts of the available analytical tools and techniques Details intercommunity advances in computational statistics and machine learning Handbook of Big Data also identifies areas in need of further development, encouraging greater communication and collaboration between researchers in big data sub-specialties such as genomics, computational biology, and finance.

Working memory - the ability to keep important information in mind while comprehending, thinking, and acting - varies considerably from person to person and changes dramatically during each person's life. Understanding such individual and developmental differences is crucial because working memory is a major contributor to general intellectual functioning. This volume offers a state-of-the-art, integrative, and comprehensive approach to understanding variation in working memory by presenting explicit, detailed comparisons of the leading theories. It incorporates views from the different research groups that operate on each side of the Atlantic, and covers working-memory research on a wide variety of populations, including healthy adults, children with and without learning difficulties, older adults, and adults and children with neurological disorders. A particular strength of this volume is that each research group explicitly addresses the same set of theoretical questions, from the perspective of both their own theoretical and experimental work and from the perspective of relevant alternative approaches. Through these questions, each research group considers their overarching theory of working memory, specifies the critical sources of working memory variation according to their theory, reflects on the compatibility of their approach with other approaches, and assesses their contribution to general working memory theory. This shared focus across chapters unifies the volume and highlights the similarities and differences among the various theories. Each chapter includes both a summary of research positions and a detailed discussion of each position. Variation in Working Memory achieves coherence across its chapters, while presenting the entire range of current theoretical and experimental approaches to variation in working memory.

Postmodern Time and Space in Fiction and Theory seeks to place the contemporary transformation of notions of space and time, often attributed to the technologies we use, in the context of the ongoing transformations of modernity. Bringing together examples of modern and contemporary fiction (from Defoe to DeLillo, Frankenstein to Finnegans Wake) and theoretical discussions of the modern and the post-modern, the author explores the legacy of modern transformations of space and time under five headings: "The Space of Nature"; "The Space of the City"; "Postmodern or Most Modern Time"; "The Time and Space of the Work of Art in the Age of Digital Reproduction"; and "Travel: from Modernity to...?". These five essays re-examine the meanings of modernity and its aftermath in relation to the spaces and times of the natural, the urban and the media environment.

This book contains the necessary information for college students to write successful research papers. Most research textbooks stop short at describing the step-by-step process of building and presenting research papers. This book does not. The textbook's design walks students through the logical process of building research papers and presenting research findings both orally and in writing. Topics include: APA Writing Guide and Paper Requirements The Purpose Statement Citing in APA Style What is a Scholarly Journal? The Literature Review Critical Thinking: Analysis, Synthesis, and Evaluation The Oral Presentation Completing the Paper The textbook serves as a primary textbook for courses involving research methods and paper writing or serves as an effective supplement to courses with major research paper components. The textbook contains several practical exercises and helpful tables as well.

Based on the highly successful Los Angeles workshop by the same name, HEAL YOUR BROKEN HEART is an easy-to-follow process that gently guides us through understanding, releasing, and ultimately healing our heartbreak from a lost romantic relationship. Michael Kane has filled his book with extraordinary tools and superb guidance we can all use. In his direct, easy tone he teaches us how to heal both our past and present heart wounding as we simultaneously learn to identify our relationship patterns. The result gives us a renewed connection to ourselves, a refreshed sense of self-confidence and personal awareness, and a healthy approach to our future relationships. HEAL YOUR BROKEN HEART is also a primer on love, clarifying what love is and inspiring us to love and nurture ourselves as we mend from our broken hearts. This is a book for both women and men that teaches us how to process through our pain and recover fully from it.

A year inside the fierce rivalries and big business of competitive videogaming A technology-fueled spin on a classic sports tale, Game Boys profiles "cyber-athletes" who compete for dominance in the professional gaming circuit, a world populated with rivalries, big egos, corporate sponsorships, and large cash prizes. Michael Kane's pioneering account of the lifestyle and business of gaming takes readers to the heart of "e-sports," what many consider the successor in sports entertainment to the X-games and competitive poker. Following the top teams-3D and CompLexity, a rivalry as bitter as the Yankees versus the Red Sox-Kane profiles the star players as they cheat, strategize, sign with rival teams, and get berated by sideline-pacing coaches. Are gamers really the athletes of tomorrow? They act like they are. A lively tour of the quirks and dramas of a subculture on the cusp of big things, Game Boys is a tale of sports glory and a glimpse into the lucrative business of gaming.

Valerie Bernowski: broken-home princess, obsessive-compulsive perfectionist, and... Catholic school survivor?
Valerie Bernowski hates her school, her plaid uniform skirt, and her flat feathered hair. She also hates being teased and called "polock" so much, she tells everyone she's Swedish.
When Valerie finds out her parents are getting divorced her world turns upside down. She begins to rebel against the Catholic faith and the ones who push it on her; her mother, Sister Mary Angelina, and even Father "Fingers."
Valerie's story begins in the mid-'80s, when new wave was big and the hairstyles were even bigger. Her tales unfold through intertwining chapters of flashbacks and present day reflections. Her bumpy road to self-discovery is paved with a cynical sense of humor, a longing for love, and a struggle to find faith.
Will Valerie realize that in order to move forward, she needs to let go of the pain of the past and the fear of her future?
Find out in CONFESSIONS OF A CATHOLIC SCHOOLGIRL

In building up a scenario for the arrival on the shores of Alaska of speakers of languages related to Eskimo-Aleut with genetic roots deep within Sineria, this book touches upon a number of issues in contemporary historical linguistics and archaeology. The Arctic "gateway" to the New World, by acting as a bottleneck, has allowed only small groups of mobile hunter-gatherers through during specific propitious periods, and thus provides a unique testing ground for theories about population and language movements in pre-agricultural times. Owing to the historically attested prevalence of language shifts and other contact phenomena in the region, it is arguable that the spread of genes and the spread of language have been out of step since the earliest reconstructable times, contrary to certain views of their linkage. Proposals that have been put forward in the past concerning the affiliations of Eskimo-Aleut languages are followed up in the light of recent progress in reconstructing the proto-languages concerned. Those linking Eskimo-Aleut with the Uralic languages and Yukagir are particularly promising, and reconstructions for many common elements are presented. The entire region "Great Beringia" is scoured for typological evidence in the form of anomalies and constellations of uncommon traits diagnostic of affiliation or contact. The various threads lead back to mesolithic times in south central Siberia, when speakers of a "Uralo-Siberian" mesh of related languages appears to have moved along the major waterways of Siberia. Such a scenario would acount for the present distribution of these languages and the results of their meeting with remnants of earlier linguistic waves from the Old World to the New.