Liu S Principles And Practice Of Laboratory Mouse Operations Filetype Pdf
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Nurse as Educator
Author: Susan Bacorn Bastable
language: en
Publisher: Jones & Bartlett Learning
Release Date: 2008
Designed to teach nurses about the development, motivational, and sociocultural differences that affect teaching and learning, this text combines theoretical and pragmatic content in a balanced, complete style. --from publisher description.
Liu's Principles and Practice of Laboratory Mouse Operations
This book fills the current void of academic writings on laboratory mouse operation, giving research scientists, graduate students, and laboratory technicians an authoritative textbook and definitive laboratory companion. It covers mouse anatomy, the handling of the mouse, anesthesia, drug administration, specimen collection, organ harvesting and daily laboratory skills as well as advanced micro-surgery techniques. Its detailed description of mouse anatomy corrects many inaccuracies and misconceptions in the literature. It provides a wealth of basic laboratory skills and numerous advanced surgical techniques. The step-by-step explanations, with extensive photographic images and videos, improve the current understanding and practice of laboratory mouse operations. This book lays the foundation of laboratory mouse operations by offering a clear understanding of the basic principles, updated anatomic studies, and providing invaluable practical tools. It serves a wide audience, including laboratory animal scientists, pharmaceutical science researchers, graduate students in these fields, micro surgeons, veterinarians, and laboratory technicians.
Reinforcement Learning, second edition
The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence. Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics. Like the first edition, this second edition focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes. Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Many algorithms presented in this part are new to the second edition, including UCB, Expected Sarsa, and Double Learning. Part II extends these ideas to function approximation, with new sections on such topics as artificial neural networks and the Fourier basis, and offers expanded treatment of off-policy learning and policy-gradient methods. Part III has new chapters on reinforcement learning's relationships to psychology and neuroscience, as well as an updated case-studies chapter including AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy. The final chapter discusses the future societal impacts of reinforcement learning.