Manasa E Tek Connect
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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.
Microfluidic Cell Culture Systems
Author: Christopher Bettinger
language: en
Publisher: William Andrew
Release Date: 2012-12-31
The fields of microfluidics and BioMEMS are significantly impacting cell biology research and applications through the application of engineering solutions to human disease and health problems. The dimensions of microfluidic channels are well suited to the physical scale of biological cells, and the many advantages of microfluidics make it an attractive platform for new techniques in biology. This new professional reference applies the techniques of microsystems to cell culture applications. The authors provide a thoroughly practical guide to the principles of microfluidic device design and operation and their application to cell culture techniques. The resulting book is crammed with strategies and techniques that can be immediately deployed in the lab. Equally, the insights into cell culture applications will provide those involved in traditional microfluidics and BioMEMS with an understanding of the specific demands and opportunities presented by biological applications. The goal is to guide new and interested researchers and technology developers to the important areas and state-of-the-practice strategies that will enhance the efficiency and value of their technologies, devices and biomedical products. - Provides insights into the design and development of microfluidic systems with a specific focus on cell culture applications - Focuses on strategies and techniques for the design and fabrication of microfluidic systems and devices for cell culture - Provides balanced coverage of microsystems engineering and bioengineering
Survey of Text Mining
Author: Michael W. Berry
language: en
Publisher: Springer Science & Business Media
Release Date: 2003-09-09
Extracting content from text continues to be an important research problem for information processing and management. Approaches to capture the semantics of text-based document collections may be based on Bayesian models, probability theory, vector space models, statistical models, or even graph theory. As the volume of digitized textual media continues to grow, so does the need for designing robust, scalable indexing and search strategies (software) to meet a variety of user needs. Knowledge extraction or creation from text requires systematic yet reliable processing that can be codified and adapted for changing needs and environments. This book will draw upon experts in both academia and industry to recommend practical approaches to the purification, indexing, and mining of textual information. It will address document identification, clustering and categorizing documents, cleaning text, and visualizing semantic models of text.