About the Book
In this extensively revised and updated edition of her classic work, Look Back in Gender, Michelene Wandor confirms the symbiotic relationship between drama and gender in a provocative look at key, representative British plays from the last fifty years. Repositioning the text at the heart of hteatre studies, Wandor surveys plays by Ayckbourn, Beckett, Churchill, Daniels, Friel, Hare, Kane, Osborne, Pinter, Ravenhill, Wertenbaker, Wesker and others. Her nuanced argument, central to any analysis of contemporary drama, discusses: *the imperative of gender in the playwright's imagination *the function of gender as a major determinant of the text's structural and narrative drives *the impact of socialism and feminism on post-war British drama, and the relevance of feminist dynamics in drama *differences in the representation of the fmaily, sexuality and the mother, before and after 1968 *the impact of the slogan that the 'personal is political' on contemporary form and content.
Table of Contents:
Contents: Preface: Multilayer Structure of the Book and Its Summaries. P. Smolensky, Overview: Computational, Dynamical, and Statistical Perspectives on the Processing and Learning Problems in Neural Network Theory. Part I: Computational Perspectives.P. Smolensky, Overview: Computational Perspectives on Neural Networks. S. Franklin, M. Garzon, Computation by Discrete Neural Nets. I. Parberry, Circuit Complexity and Feedforward Neural Networks. J.S. Judd, Complexity of Learning. E.H.L Aarts, J.H.M. Korst, P.J. Zwietering, Deterministic and Randomized Local Search. M.B. Pour-El, The Mathematical Theory of the Analog Computer. Part II: Dynamical Perspectives.P. Smolensky, Overview: Dynamical Perspectives on Neural Networks. M.W. Hirsch, Dynamical Systems. L.F. Abbott, Statistical Analysis of Neural Networks. K.S. Narendra, S-M. Li, Neural Networks in Control Systems. A.S. Weigend, Time Series Analysis and Prediction. Part III: Statistical Perspectives.P. Smolensky, Overview: Statistical Perspectives on Neural Networks. R. Szeliski, Regularization in Neural Nets. D.E. Rumelhart, R. Durbin, R. Goldin, Y. Chauvin, Backpropagation: The Basic Theory. J. Rissanen, Information Theory and Neural Nets. A. Nádas, R.L. Mercer, Hidden Markov Models and Some Connections with Artificial Neural Nets. D. Haussler, Probably Approximately Correct Learning and Decision-Theoretic Generalizations. H. White, Parametric Statistical Estimation with Artificial Neural Networks. V.N. Vapnik, Inductive Principles of Statistics and Learning Theory.
About the Author :
Paul Smolensky, Michael C. Mozer, David E. Rumelhart
Review :
Although the material is advanced and technical, the volume has a "multilayer" structure including a general overview, and there are overviews of each of the main parts (including summary tables of key results). These surveys, all written by Smolensky (John Hopkins University), provide an excellent introduction to the papers and their context, making the material accessible to upper-division undergraduates, graduate students, or faculty.
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