[FREE] 76 Fallacies download book
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Dr Michael Cooper Labossiere: 76 Fallacies
Description
The book presents the following 73 informal fallacies: Accent, Fallacy of Accident, Fallacy of Ad Hominem Ad Hominem Tu Quoque Amphiboly, Fallacy of Anecdotal Evidence, Fallacy Of Appeal to the Consequences of a Belief Appeal to Authority, Fallacious Appeal to Belief Appeal to Common Practice Appeal to Emotion Appeal to Envy Appeal to Fear Appeal to Flattery Appeal to Group Identity Appeal to Guilt Appeal to Novelty Appeal to Pity Appeal to Popularity Appeal to Ridicule Appeal to Spite Appeal to Tradition Appeal to Silence Appeal to Vanity Argumentum ad Hitlerum Begging the Question Biased Generalization Burden of Proof Complex Question Composition, Fallacy of Confusing Cause and Effect Confusing Explanations and Excuses Circumstantial Ad Hominem Cum Hoc, Ergo Propter Hoc Division, Fallacy of Equivocation, Fallacy of Fallacious Example Fallacy Fallacy False Dilemma Gambler's Fallacy Genetic Fallacy Guilt by Association Hasty Generalization Historian's Fallacy Illicit Conversion Ignoring a Common Cause Incomplete Evidence Middle Ground Misleading Vividness Moving the Goal Posts Oversimplified Cause Overconfident Inference from Unknown Statistics Pathetic Fallacy Peer Pressure Personal Attack Poisoning the Well Positive Ad Hominem Post Hoc Proving X, Concluding Y Psychologist's fallacy Questionable Cause Rationalization Red Herring Reification, Fallacy of Relativist Fallacy Slippery Slope Special Pleading Spotlight Straw Man Texas Sharpshooter Fallacy Two Wrongs Make a Right Victim Fallacy Weak Analogy The book contains the following three formal (deductive) fallacies: Affirming the Consequent Denying the Antecedent Undistributed Middle
"I'm not a businessman-I'm a business, man." 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 when interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications. The only necessary mathematical background is familiarity with elementary concepts of probability. The book is divided into three parts. Part I defines the reinforcement 76 Fallacies download epub learning problem in terms of Markov decision processes. Part II provides basic solution methods: dynamic programming, Monte Carlo methods, and temporal-difference learning. Part III presents a unified view of the solution methods and incorporates artificial neural networks, eligibility traces, and planning; the two final chapters present case studies and consider the future of reinforcement learning.
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Author: Dr Michael Cooper Labossiere
Number of Pages: 134 pages
Published Date: 16 Mar 2013
Publisher: Createspace
Publication Country: United States
Language: English
ISBN: 9781482786248
Download Link: Click Here
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