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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 a

Improved conditions of care for premature infants have led to markedly increased survival rates over the last few decades, particularly in very low and extremely low birth weight infants. Nutritional measures play a central role in the long-term outcome, health, and quality of life of these premature infants. In this updated and extended edition, leading experts from all over the world present the most recent evidence and critical analyses of nutrient requirements and the practice of nutritional care (with the focus on very low birth weight infants) to provide guidance for clinical application. The chapters of this publication show how growth and development can be nutritionally supported, which nutrients and non-nutrients can be supplied, and how nutritional care can get implemented. Approaches to nutritional care in various disease conditions are also addressed. The compilation of current information and recommendations should support the daily work of health care professionals such

This book provides a comprehensive foundation of machine learning. To answer the questions of what to learn, how to learn, what to get from learning, and how to evaluate, as well as what is meant by learning, the book focuses on the fundamental basics of machine learning, its methodology, theory, algorithms, and evaluations, together with some philosophical thinking on comparison between machine learning and human learning for machinery intelligence.The book is organized as follows: Introduction (Chapter 1), Evaluation (Chapter 2), Supervised learning (Chapters 3, 4, and 5), Unsupervised learning (Chapter 6), Representation learning (Chapter 7), Problem decomposition (Chapter 8), Ensemble learning (Chapter 9), Deep learning (Chapter 10), Application (Chapter 11), and Challenges (Chapter 12).The book can be used as a textbook for college, undergraduate, graduate and PhD students majored in computer science, automation, electronic engineering, communication, ect. It can also be used as