This Brief presents an historical investigation into the reaction between ferric ions and thiocyanate ions, which has been viewed in different ways throughout the last two centuries. Historically, the reaction was used in chemical analysis and to highlight the nature of chemical reactions, the laws of chemistry, models and theories of chemistry, chemical nomenclature, mathematics and data analysis, and instrumentation, which are important ingredients of what one might call the nature of chemistry. Using the history of the iron(III) thiocyanate reaction as a basis, the book’s main objective is to explore how chemistry develops its own knowledge base; how it assesses the reliability of that base; and how some important tools of the trade have been brought to bear on a chemical reaction to achieve understanding, a worthwhile goal of any historical investigation.
This Brief presents an historical investigation into the reaction between ferric ions and thiocyanate ions, which has been viewed in different ways throughout the last two centuries. Historically, the reaction was used in chemical analysis and to highlight the nature of chemical reactions, the laws of chemistry, models and theories of chemistry, chemical nomenclature, mathematics and data analysis, and instrumentation, which are important ingredients of what one might call the nature of chemistry. Using the history of the iron(III) thiocyanate reaction as a basis, the book’s main objective is to explore how chemistry develops its own knowledge base; how it assesses the reliability of that base; and how some important tools of the trade have been brought to bear on a chemical reaction to achieve understanding, a worthwhile goal of any historical investigation.
This book examines comparatively how the writing of history has been used to 'legitimate' the nation-state against socialist, communist and catholic internationalism in the modern era.
In Material Dreams, Starr turns to one of the most vibrant decades in the Golden State's history, the 1920s, when some two million Americans migrated to California, the vast majority settling in or around Los Angeles. Although he treats readers to intriguing side trips to Santa Barbara and Pasadena, Starr focuses here mainly on Los Angeles, revealing how this major city arose almost defiantly on a site lacking many of the advantages required for urban development, creating itself out of sheer will, the Great Gatsby of American cities. He describes how William Ellsworth Smyth, the Peter the Hermit of the Irrigation Crusade, propounded the importance of water in Southern California's future, and how such figures as the self-educated, Irish engineer William Mulholland (who built the main aquaducts to Los Angeles) and George Chaffey (who diverted the Colorado River, transforming desert into the lush Imperial Valley) brought life-supporting water to the arid South. He examines the discovery of oil ("Yes it's oil, oil, oil / that makes LA boil," went the official drinking song of the Uplifters Club), the boosters and land developers, the evangelists (such as Bob Shuler, the Methodist Savanarola of Los Angeles, and Aimee Semple McPherson), and countless other colorful figures of the period. There are also fascinating sections on the city's architecture (such as the remarkably innovative Bradbury Building and its eccentric, neophyte designer, George Wyman), the impact of the automobile on city planning, the great antiquarian book collections, the Hollywood film community, and much more. By the end of the decade, Los Angeles had tripled in population and become the fifth largest city in the nation. In Material Dreams, Kevin Starr captures this explosive growth in a narrative tour de force that combines wide-ranging scholarship with captivating prose.
A detailed and up-to-date introduction to machine learning, presented through the unifying lens of probabilistic modeling and Bayesian decision theory. This book offers a detailed and up-to-date introduction to machine learning (including deep learning) through the unifying lens of probabilistic modeling and Bayesian decision theory. The book covers mathematical background (including linear algebra and optimization), basic supervised learning (including linear and logistic regression and deep neural networks), as well as more advanced topics (including transfer learning and unsupervised learning). End-of-chapter exercises allow students to apply what they have learned, and an appendix covers notation. Probabilistic Machine Learning grew out of the author’s 2012 book, Machine Learning: A Probabilistic Perspective. More than just a simple update, this is a completely new book that reflects the dramatic developments in the field since 2012, most notably deep learning. In addition, the new book is accompanied by online Python code, using libraries such as scikit-learn, JAX, PyTorch, and Tensorflow, which can be used to reproduce nearly all the figures; this code can be run inside a web browser using cloud-based notebooks, and provides a practical complement to the theoretical topics discussed in the book. This introductory text will be followed by a sequel that covers more advanced topics, taking the same probabilistic approach.
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