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Deep learning (Machine learning)

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"Algebraic Structures in Natural Language addresses a central problem in cognitive science, concerning the learning procedures through which humans acquire and represent natural language. Until recently algebraic systems have dominated the study of natural language in formal and computational linguistics, AI, and the of psychology of language, with linguistic knowledge seen as encoded in formal grammars, model theories, proof theories, and other rule driven devices, and researchers drawing conclusions about how humans acquire and represent language. Recent work on deep learning has produced an increasingly powerful set of general learning mechanisms which do not apply algebraic models of representation (although they can be combined with them), and success in NLP in particular has led some researchers to question the role of algebraic models in the study of human language acquisition and linguistic representation. Psychologists and cognitive scientists have also been exploring explanations of language evolution and language acquisition that rely on probabilistic methods, social interaction, and information theory, rather than on formal models of grammar induction. This work has also led some researchers to question the centrality of algebraic approaches to linguistic representation. This book addresses the learning procedures through which humans acquire natural language, and the way in which they represent its properties. It brings together leading researchers from computational linguistics, psychology, behavioural science, and mathematical linguistics to consider the significance of non-algebraic methods for the study of natural language, and represents a wide spectrum of views, from the claim that algebraic systems are largely irrelevant, to the contrary position that non-algebraic learning methods are engineering devices for efficiently identifying the patterns that underlying grammars and semantic models generate for natural language input. There are interesting and important perspectives that fall at intermediate points between these opposing approaches, and they may combine elements of both. It will appeal to researchers and advanced students in each of these fields, as well as to anyone who wants to learn more about the relationship between algorithms and language"--

Language acquisition --- Mathematical linguistics --- Deep learning (Machine learning)

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"Providing a logical framework for student learning, this is the first textbook on adversarial learning. It introduces vulnerabilities of deep learning, then demonstrates methods for defending against attacks and making AI generally more robust. To help students connect theory with practice, it explains and evaluates attack-and-defense scenarios alongside real-world examples. Feasible, hands-on student projects, which increase in difficulty throughout the book, give students practical experience and help to improve their Python and PyTorch skills. Book chapters conclude with questions that can be used for classroom discussions. In addition to deep neural networks, students will also learn about logistic regression, naïve Bayes classifiers, and support vector machines. Written for senior undergraduate and first-year graduate courses, the book offers a window into research methods and current challenges. Online resources include lecture slides and image files for instructors, and software for early course projects for students."

Machine learning --- Artificial intelligence --- Computer security --- Deep learning (Machine learning) --- Artificial intelligence. --- Apprentissage automatique. --- Intelligence artificielle.

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En matière de nouvelles technologies, l'IA générative est le sujet dont on parle le plus en ce moment. Ce livre pratique enseigne aux ingénieurs en apprentissage automatique et aux data scientists l'utilisation de TensorFlow et de Keras pour créer de puissants modèles de deep learning génératif en partant de zéro, notamment des autoencodeurs variationnels (VAE), des réseaux antagonistes génératifs (GAN), des modèles Transformer, des flux de normalisation, des modèles basés sur l'énergie et des modèles de diffusion de débruitage.

Machine learning --- Artificial intelligence --- Neural networks (Computer science) --- Generative programming (Computer science) --- Deep learning (Machine learning)

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The recent rapid growth in the variety and complexity of new machine learning architectures requires the development of improved methods for designing, analyzing, evaluating, and communicating machine learning technologies. Statistical Machine Learning: A Unified Framework provides students, engineers, and scientists with tools from mathematical statistics and nonlinear optimization theory to become experts in the field of machine learning. In particular, the material in this text directly supports the mathematical analysis and design of old, new, and not-yet-invented nonlinear high-dimensional machine learning algorithms. Features: Unified empirical risk minimization framework supports rigorous mathematical analyses of widely used supervised, unsupervised, and reinforcement machine learning algorithms Matrix calculus methods for supporting machine learning analysis and design applications Explicit conditions for ensuring convergence of adaptive, batch, minibatch, MCEM, and MCMC learning algorithms that minimize both unimodal and multimodal objective functions Explicit conditions for characterizing asymptotic properties of M-estimators and model selection criteria such as AIC and BIC in the presence of possible model misspecification This advanced text is suitable for graduate students or highly motivated undergraduate students in statistics, computer science, electrical engineering, and applied mathematics. The text is self-contained and only assumes knowledge of lower-division linear algebra and upper-division probability theory. Students, professional engineers, and multidisciplinary scientists possessing these minimal prerequisites will find this text challenging yet accessible. About the Author: Richard M. Golden (Ph.D., M.S.E.E., B.S.E.E.) is Professor of Cognitive Science and Participating Faculty Member in Electrical Engineering at the University of Texas at Dallas. Dr. Golden has published articles and given talks at scientific conferences on a wide range of topics in the fields of both statistics and machine learning over the past three decades. His long-term research interests include identifying conditions for the convergence of deterministic and stochastic machine learning algorithms and investigating estimation and inference in the presence of possibly misspecified probability models.

Machine learning --- Computer algorithms. --- Algorithms --- Learning, Machine --- Artificial intelligence --- Machine theory --- Statistical methods. --- Machine learning - Statistical methods.