Model Rules of Professional Conduct
Model Rules of Professional Conduct

Author: American Bar Association. House of Delegates

Publisher: American Bar Association

Published: 2007

Total Pages: 216

ISBN-13: 9781590318737

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The Model Rules of Professional Conduct provides an up-to-date resource for information on legal ethics. Federal, state and local courts in all jurisdictions look to the Rules for guidance in solving lawyer malpractice cases, disciplinary actions, disqualification issues, sanctions questions and much more. In this volume, black-letter Rules of Professional Conduct are followed by numbered Comments that explain each Rule's purpose and provide suggestions for its practical application. The Rules will help you identify proper conduct in a variety of given situations, review those instances where discretionary action is possible, and define the nature of the relationship between you and your clients, colleagues and the courts.

The Little Book of Plagiarism
The Little Book of Plagiarism

Author: Richard A. Posner

Publisher: Pantheon

Published: 2009-03-12

Total Pages: 130

ISBN-13: 0307496538

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A concise, lively, and bracing exploration of an issue bedeviling our cultural landscape–plagiarism in literature, academia, music, art, and film–by one of our most influential and controversial legal scholars. Best-selling novelists J. K. Rowling and Dan Brown, popular historians Doris Kearns Goodwin and Stephen Ambrose, Harvard law professor Charles Ogletree, first novelist Kaavya Viswanathan: all have rightly or wrongly been accused of plagiarism–theft of intellectual property–provoking widespread media punditry. But what exactly is plagiarism? How has the meaning of this notoriously ambiguous term changed over time as a consequence of historical and cultural transformations? Is the practice on the rise, or just more easily detectable by technological advances? How does the current market for expressive goods inform our own understanding of plagiarism? Is there really such a thing as “cryptomnesia,” the unconscious, unintentional appropriation of another’s work? What are the mysterious motives and curious excuses of plagiarists? What forms of punishment and absolution does this “sin” elicit? What is the good in certain types of plagiarism? Provocative, insightful, and extraordinary for its clarity and forthrightness, The Little Book of Plagiarism is an analytical tour de force in small, the work of “one of the top twenty legal thinkers in America” (Legal Affairs), a distinguished jurist renowned for his adventuresome intellect and daring iconoclasm.

Deep Learning
Deep Learning

Author: Ian Goodfellow

Publisher: MIT Press

Published: 2016-11-10

Total Pages: 801

ISBN-13: 0262337371

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An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.