Affective Encounters (LSE Monographs on Social Anthropology)

Wu, Di

Reference

Routledge

Data Mining with Python (Chapman & Hall/CRC The Python Series)

Computers & Technology

Computer Science

Chapman and Hall/CRC

Qigong Journey brings students of Asian culture, Daoist philosophy, and the mind/body wellness connection directly into the mind of a master. Through the unparalleled knowledge and openness of Dr. Baolin Wu, take a journey through the many layers of the Daoist qigong experience. In this expansive volume, filled with personal anecdotes of life in the White Cloud Monastery, lies an in-depth exploration of the holistic and lyrical Daoist worldview, a shamanic experience both pragmatic and poetic. Discover the connection between the history and traditions of ancient understanding and the daily life we all lead today. The book is a practical manual detailing a complete, eight hundred year old protective qigong practice from the founder of the White Cloud Monastery itself, preserved in silence by the members of the White Cloud Monastery community until today. With painstaking, step-by-step instructions and beautiful illustrations by Oliver Benson, our beloved Qi Gong for Total Wellness artist, the goal of this book is to present the next level of Nine Palaces Daoist Qigong. Using a guided series of breath, visualization and internal isometric strengthening exercises, it is a life-changing physical training, focused on securing one's body and consciousness in the midst of the very real health challenges, hardships and volatility of today's world.

Qigong Journey: Nine-Five Maintenance of Qi

Health, Fitness & Dieting

Wu, Baolin

Eckstein, Jessica

Exercise & Fitness

Three Pines Press

U.S. Public Diplomacy Towards China: Exercising Discretion in Educational and Exchange Programs (Palgrave Macmillan Series in Global Public Diplomacy)

PALGRAVE~MACMILLAN

Wu Di said I know how you were left over, wont you feel sharp pain when hearing this? The seemingly toneless family dialogue and event is pricking your heart and reminding you of the fact that you are left over. Any problem-solving is like disease curing. Wu Di is just the best love doctor though very sharp. In this book, Wu cited tens of real cases from reform of single girls, to marriage battle, and analyzed them one by one to point out the crux and give correspondent prescriptions. Love letter is absolutely to read; complex to solve; and love doctor to visit.

I Know How You Are Left Over (Chinese Edition)

Guangming Daily Press

Children aged 0 to open intellectual enlightenment larger card card(Chinese Edition)

Liaoning Children's Pub. Date :2011-10-01 version 1

Incomplete big data are frequently encountered in many industrial applications, such as recommender systems, the Internet of Things, intelligent transportation, cloud computing, and so on. It is of great significance to analyze them for mining rich and valuable knowledge and patterns. Latent feature analysis (LFA) is one of the most popular representation learning methods tailored for incomplete big data due to its high accuracy, computational efficiency, and ease of scalability. The crux of analyzing incomplete big data lies in addressing the uncertainty problem caused by their incomplete characteristics. However, existing LFA methods do not fully consider such uncertainty. In this book, the author introduces several robust latent feature learning methods to address such uncertainty for effectively and efficiently analyzing incomplete big data, including robust latent feature learning based on smooth L1-norm, improving robustness of latent feature learningusing L1-norm, improving robustness of latent feature learning using double-space, data-characteristic-aware latent feature learning, posterior-neighborhood-regularized latent feature learning, and generalized deep latent feature learning. Readers can obtain an overview of the challenges of analyzing incomplete big data and how to employ latent feature learning to build a robust model to analyze incomplete big data. In addition, this book provides several algorithms and real application cases, which can help students, researchers, and professionals easily build their models to analyze incomplete big data.

Robust Latent Feature Learning for Incomplete Big Data (SpringerBriefs in Computer Science)

Springer

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Books by Wu, Di