Revision And Self Editing For Publication Second Edition Writer S Digest Books 2012 This Book Discusses Various Methods Of Self Editing And Revising Works Of Fiction Bell Explains His Techniques With Illustrations And Exercises With Numerous References And Examples Moreover Bell Includes Self Editing And Revision Exercises For Practice This Text Is Instructive In The Critical Paper And The Creative Manuscript

Plot & Structure (Write Great Fiction)

Craft an Engaging Plot How does plot influence story structure? What's the difference between plotting for commercial and literary fiction? How do you revise a plot or structure that's gone off course? With Write Great Fiction: Plot & Structure, you'll discover the answers to these questions and more. Award-winning author James Scott Bell offers clear, concise information that will help you create a believable and memorable plot, including: • Techniques for crafting strong beginnings, middles, and ends • Easy-to-understand plotting diagrams and charts • Brainstorming techniques for original plot ideas • Thought-provoking exercises at the end of each chapter • Story structure models and methods for all genres • Tips and tools for correcting common plot problems Filled with plot examples from popular novels, comprehensive checklists, and practical hands-on guidance, Write Great Fiction: Plot & Structure gives you the skills you need to approach plot and structure like an experienced pro.

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.

Grammar of the Edit