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Demonstrating the vibrancy of an Early Modern Muslim society through a study of the natural sciences in seventeenth-century Morocco, Revealed Sciences examines how the natural sciences flourished during this period, without developing in a similar way to the natural sciences in Europe. Offering an innovative analysis of the relationship between religious thought and the natural sciences, Justin K. Stearns shows how nineteenth and twentieth-century European and Middle Eastern scholars jointly developed a narrative of the decline of post-formative Islamic thought, including the fate of the natural sciences in the Muslim world. Challenging these depictions of the natural sciences in the Muslim world, Stearns uses numerous close readings of works in the natural sciences to a detailed overview of the place of the natural sciences in scholarly and educational landscapes of the Early Modern Magreb, and considers non-teleological possibilities for understanding a persistent engagement with the natural sciences in Early Modern Morocco.
Provides a detailed overview of the place of the natural sciences in the scholarly and educational landscape of Early Modern Morocco, this study challenges previous negative depictions of the natural sciences in the Muslim world to demonstrate the vibrancy of an Early Modern Muslim society in seventeenth-century Morocco.
Study of Thomas Jefferson as a scientist, including the various branches of science he studied and to which he made lasting contributions. Also examines how science shaped his views on the politics, religion, economics, and social developments in his own country.
Includes section "Reviews of recent theological literature".
Get insight into data science techniques such as data engineering and visualization, statistical modeling, machine learning, and deep learning. This book teaches you how to select variables, optimize hyper parameters, develop pipelines, and train, test, and validate machine and deep learning models. Each chapter includes a set of examples allowing you to understand the concepts, assumptions, and procedures behind each model. The book covers parametric methods or linear models that combat under- or over-fitting using techniques such as Lasso and Ridge. It includes complex regression analysis with time series smoothing, decomposition, and forecasting. It takes a fresh look at non-parametric models for binary classification (logistic regression analysis) and ensemble methods such as decision trees, support vector machines, and naive Bayes. It covers the most popular non-parametric method for time-event data (the Kaplan-Meier estimator). It also covers ways of solving classification problems using artificial neural networks such as restricted Boltzmann machines, multi-layer perceptrons, and deep belief networks. The book discusses unsupervised learning clustering techniques such as the K-means method, agglomerative and Dbscan approaches, and dimension reduction techniques such as Feature Importance, Principal Component Analysis, and Linear Discriminant Analysis. And it introduces driverless artificial intelligence using H2O. After reading this book, you will be able to develop, test, validate, and optimize statistical machine learning and deep learning models, and engineer, visualize, and interpret sets of data. What You Will Learn Design, develop, train, and validate machine learning and deep learning models Find optimal hyper parameters for superior model performance Improve model performance using techniques such as dimension reduction and regularization Extract meaningful insights for decision making using data visualization Who This Book Is For Beginning and intermediate level data scientists and machine learning engineers
This is the first comprehensive overview of the exciting field of the 'science of science'. With anecdotes and detailed, easy-to-follow explanations of the research, this book is accessible to all scientists, policy makers, and administrators with an interest in the wider scientific enterprise.
A mind-bending excursion to the limits of science and mathematics Are some scientific problems insoluble? In Beyond Reason, internationally acclaimed math and science author A. K. Dewdney answers this question by examining eight insurmountable mathematical and scientific roadblocks that have stumped thinkers across the centuries, from ancient mathematical conundrums such as "squaring the circle," first attempted by the Pythagoreans, to G?del's vexing theorem, from perpetual motion to the upredictable behavior of chaotic systems such as the weather. A. K. Dewdney, PhD (Ontario, Canada), was the author of Scientific American's "Computer Recreations" column for eight years. He has written several critically acclaimed popular math and science books, including A Mathematical Mystery Tour (0-471-40734-8); Yes, We Have No Neutrons (0-471-29586-8); and 200% of Nothing (0-471-14574-2).
Is your worldview enlightened enough to accommodate both science and God at the same time? Dr. Michael Guillen, a best-selling author, Emmy award–winning journalist and former physics instructor at Harvard, used to be an Atheist—until science changed his mind. Once of the opinion that people of faith are weak, small-minded folks who just don’t understand science, Dr. Guillen ultimately concluded that not only does science itself depend on faith, but faith is actually the mightiest power in the universe. In Believing Is Seeing, Dr. Guillen recounts the fascinating story of his journey from Atheism to Christianity, citing the latest discoveries in neuroscience, physics, astronomy, and mathematics to pull back the curtain on the mystery of faith as no one ever has. Is it true that “seeing is believing?” Or is it possible that reality can be perceived most clearly with the eyes of faith—and that truth is bigger than proof? Let Dr. Guillen be your guide as he brilliantly argues for a large and enlightened worldview consistent with both God and modern science.