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In this wide-ranging and ambitious volume, Robert Royal, a prominent participant for many years in debates about religion and contemporary life, offers a comprehensive and balanced appraisal of the Catholic intellectual tradition in the twentieth century. The Catholic Church values both Faith and Reason, and Catholicism has given risen to extraordinary ideas and whole schools of remarkable thought, not just in the distant past but throughout the troubled decades of the twentieth century. Royal presents in a single volume a sweeping but readable account of how Catholic thinking developed in philosophy, theology, Scripture studies, culture, literature, and much more in the twentieth century. This involves great figures, recognized as such both inside and outside the Church, such as Jacques Maritain, Bernard Lonergan, Joseph Pieper, Edith Stein, Alasdair MacIntyre, Charles Taylor, Romano Guardini, Karl Rahner, Henri du Lubac, Karol Wojtyla, Joseph Ratzinger, Hans Urs von Balthasar,Charles Peguy, Paul Claudel, George Bernanos, Francois Mauriac, G. K. Chesterton, Gerard Manley Hopkins, Christopher Dawson, Graham Greene, Sigrid Undset, J. R. R. Tolkien, Czeslaw Milosz, and many more. Royal argues that without rigorous thought, Catholicism – however welcoming and nourishing it might be – would become something like a doctor with a good bedside manner, but who knows little medicine. It has always been the aspiration of the Catholic tradition to unite emotion and intellect, action and contemplation. But unless we know what the tradition has already produced – especially in the work of the great figures of the recent past – we will not be able to answer the challenges that the modern world poses, or even properly recognize the true questions we face. This is a reflective, non-polemical work that brings together various strands of Catholic thought in the twentieth century. A comprehensive guide to the recent past - and the future.
Millions of photos are taken every day across the world. Some are just snapshots. Others are more carefully crafted and have the capacity to deepen our vision and sharpen our sense of what life is truly about. Spirituality in Photography explores how photography can offer unique perspectives on the self, the world, and what we live by. Readers are invited to take photos more slowly, reframe their gaze, allow themselves to be fully present, and let their photos tell a story--this includes a section on how photography can offer new perspectives on the Bible. The book also explores how photography can help readers meditate on mystery and tune into their own 'rule of life.' Spirituality in Photography offers a range of ideas for readers to try out with their camera or smartphone and invites them on a journey of discovery, as they allow their photography and their spirituality to inspire each other.
Josef Sudek, the 'Poet of Prague', had a legendary career spanning almost six decades. His craftsmanship and technical virtuosity were unparalleled among his contemporaries. Faced with the legacy of cubism, surrealism and the Czech avantgarde, Sudek sought his own approach, characterized by a striking mastery of light.
Toni Morrison's Spiritual Vision unpacks an oft-ignored but essential element of her work--her religion--and in so doing gives readers a deeper, richer understanding of her life and her writing. Nadra Nittle's wide-ranging, deep exploration of Morrison's oeuvre reveals the role of religion and spirituality in her life and literature.
When one reaches the highest degree of human maturity, one has only one question left: How can I be helpful?—TERESA OF ÁVILA Beloved author Ronald Rolheiser continues his search for an accessible and penetrating Christian spirituality in this highly anticipated follow-up to the contemporary classic, The Holy Longing. With his trademark acuity, wit, and thoughtfulness, Rolheiser shows how identifying and embracing discipleship will lead to new heights of spiritual awareness and maturity. In this new book, Rolheiser takes us on a journey through the dark night of the senses and of the spirit. Here, we experience the full gamut of human life, pleasure and fervor, disillusionment and boredom. But, as Rolheiser explains, when we embrace the struggle and yearning to know God we can experience too a profound re-understanding to our daily lives. “What lies beyond the essentials, the basics?” Rolheiser writes. “Where do we go once some of the basic questions in our lives have been answered, or at least brought to enough peace that our focus can shift away from ourselves to others? Where do we go once the basic questions in our lives are no longer the restless questions of youthful insecurity and loneliness? Who am I? Who loves me? How will my life turn out? Where do we go once the basic question in life becomes: How can I give my life away more purely, and more meaningfully? How do I live beyond my own heartaches, headaches, and obsessions so as to help make other peoples’ lives more meaningful? The intent of this book is to try to address exactly those questions: How can we live less self- centered, more mature lives? What constitutes deep maturity and how do we reach that place? And, not unimportantly, what constitutes a more adult, Christian discipleship? What constitutes a truly mature following of Jesus?” As the poet Rainer Maria Rilke suggests, “Live the questions now.” In Sacred Fire, Rolheiser’s deeply affecting prose urges us on in pursuit of the most holy of all passions—a deep and lasting intimacy with God.
How does the computer learn to understand what it sees? Deep Learning for Vision Systems answers that by applying deep learning to computer vision. Using only high school algebra, this book illuminates the concepts behind visual intuition. You'll understand how to use deep learning architectures to build vision system applications for image generation and facial recognition. Summary Computer vision is central to many leading-edge innovations, including self-driving cars, drones, augmented reality, facial recognition, and much, much more. Amazing new computer vision applications are developed every day, thanks to rapid advances in AI and deep learning (DL). Deep Learning for Vision Systems teaches you the concepts and tools for building intelligent, scalable computer vision systems that can identify and react to objects in images, videos, and real life. With author Mohamed Elgendy's expert instruction and illustration of real-world projects, you’ll finally grok state-of-the-art deep learning techniques, so you can build, contribute to, and lead in the exciting realm of computer vision! Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology How much has computer vision advanced? One ride in a Tesla is the only answer you’ll need. Deep learning techniques have led to exciting breakthroughs in facial recognition, interactive simulations, and medical imaging, but nothing beats seeing a car respond to real-world stimuli while speeding down the highway. About the book How does the computer learn to understand what it sees? Deep Learning for Vision Systems answers that by applying deep learning to computer vision. Using only high school algebra, this book illuminates the concepts behind visual intuition. You'll understand how to use deep learning architectures to build vision system applications for image generation and facial recognition. What's inside Image classification and object detection Advanced deep learning architectures Transfer learning and generative adversarial networks DeepDream and neural style transfer Visual embeddings and image search About the reader For intermediate Python programmers. About the author Mohamed Elgendy is the VP of Engineering at Rakuten. A seasoned AI expert, he has previously built and managed AI products at Amazon and Twilio. Table of Contents PART 1 - DEEP LEARNING FOUNDATION 1 Welcome to computer vision 2 Deep learning and neural networks 3 Convolutional neural networks 4 Structuring DL projects and hyperparameter tuning PART 2 - IMAGE CLASSIFICATION AND DETECTION 5 Advanced CNN architectures 6 Transfer learning 7 Object detection with R-CNN, SSD, and YOLO PART 3 - GENERATIVE MODELS AND VISUAL EMBEDDINGS 8 Generative adversarial networks (GANs) 9 DeepDream and neural style transfer 10 Visual embeddings
Deep learning algorithms have brought a revolution to the computer vision community by introducing non-traditional and efficient solutions to several image-related problems that had long remained unsolved or partially addressed. This book presents a collection of eleven chapters where each individual chapter explains the deep learning principles of a specific topic, introduces reviews of up-to-date techniques, and presents research findings to the computer vision community. The book covers a broad scope of topics in deep learning concepts and applications such as accelerating the convolutional neural network inference on field-programmable gate arrays, fire detection in surveillance applications, face recognition, action and activity recognition, semantic segmentation for autonomous driving, aerial imagery registration, robot vision, tumor detection, and skin lesion segmentation as well as skin melanoma classification. The content of this book has been organized such that each chapter can be read independently from the others. The book is a valuable companion for researchers, for postgraduate and possibly senior undergraduate students who are taking an advanced course in related topics, and for those who are interested in deep learning with applications in computer vision, image processing, and pattern recognition.
Advanced Methods and Deep Learning in Computer Vision presents advanced computer vision methods, emphasizing machine and deep learning techniques that have emerged during the past 5–10 years. The book provides clear explanations of principles and algorithms supported with applications. Topics covered include machine learning, deep learning networks, generative adversarial networks, deep reinforcement learning, self-supervised learning, extraction of robust features, object detection, semantic segmentation, linguistic descriptions of images, visual search, visual tracking, 3D shape retrieval, image inpainting, novelty and anomaly detection. This book provides easy learning for researchers and practitioners of advanced computer vision methods, but it is also suitable as a textbook for a second course on computer vision and deep learning for advanced undergraduates and graduate students. - Provides an important reference on deep learning and advanced computer methods that was created by leaders in the field - Illustrates principles with modern, real-world applications - Suitable for self-learning or as a text for graduate courses
An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.
Introductory Analysis addresses the needs of students taking a course in analysis after completing a semester or two of calculus, and offers an alternative to texts that assume that math majors are their only audience. By using a conversational style that does not compromise mathematical precision, the author explains the material in terms that help the reader gain a firmer grasp of calculus concepts.* Written in an engaging, conversational tone and readable style while softening the rigor and theory* Takes a realistic approach to the necessary and accessible level of abstraction for the secondary education students* A thorough concentration of basic topics of calculus* Features a student-friendly introduction to delta-epsilon arguments * Includes a limited use of abstract generalizations for easy use* Covers natural logarithms and exponential functions* Provides the computational techniques often encountered in basic calculus