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Artificial Intelligence Engines
Dive into the fundamentals of artificial intelligence engines, covering deep learning and its mathematical underpinnings. Practical examples and tutorials help bridge the gap between theoretical AI concepts and applications. Key Features Comprehensive breakdown of foundational and advanced AI algorithms with practical insights. Clear explanations of deep learning architectures, including GANs, autoencoders, and CNNs. Step-by-step guidance on implementing mathematical concepts in real-world AI applications. Book DescriptionThis book is a comprehensive guide to the mathematics behind artificial intelligence engines, taking readers from foundational concepts to advanced applications. It begins ...
Information Theory
Learn the fundamentals of information theory, including entropy, coding, and data compression, while exploring advanced topics like transfer entropy, thermodynamics, and real-world applications. Key Features A clear blend of foundational theory and advanced topics suitable for various expertise levels A focus on practical examples to complement theoretical concepts and enhance comprehension Comprehensive coverage of applications, including data compression, thermodynamics, and biology Book DescriptionThis book offers a comprehensive journey through the fascinating world of information theory, beginning with the fundamental question: what is information? Early chapters introduce key concepts ...
Bayes' Rule
In this richly illustrated book, the tutorial style of writing, combined with a comprehensive glossary, makes this an ideal primer for the novice who wishes to become familiar with the basic principles of Bayesian analysis.
Seeing, second edition
An accessible yet rigorous and generously illustrated exploration of the computational approach to the study of biological vision. Seeing has puzzled scientists and philosophers for centuries and it continues to do so. This new edition of a classic text offers an accessible but rigorous introduction to the computational approach to understanding biological visual systems. The authors of Seeing, taking as their premise David Marr's statement that “to understand vision by studying only neurons is like trying to understand bird flight by studying only feathers,” make use of Marr's three different levels of analysis in the study of vision: the computational level, the algorithmic level, and ...
Independent Component Analysis
A tutorial-style introduction to a class of methods for extracting independent signals from a mixture of signals originating from different physical sources; includes MatLab computer code examples. Independent component analysis (ICA) is becoming an increasingly important tool for analyzing large data sets. In essence, ICA separates an observed set of signal mixtures into a set of statistically independent component signals, or source signals. In so doing, this powerful method can extract the relatively small amount of useful information typically found in large data sets. The applications for ICA range from speech processing, brain imaging, and electrical brain signals to telecommunications...
Vision and Brain
An engaging introduction to the science of vision that offers a coherent account of vision based on general information processing principles In this accessible and engaging introduction to modern vision science, James Stone uses visual illusions to explore how the brain sees the world. Understanding vision, Stone argues, is not simply a question of knowing which neurons respond to particular visual features, but also requires a computational theory of vision. Stone draws together results from David Marr's computational framework, Barlow's efficient coding hypothesis, Bayesian inference, Shannon's information theory, and signal processing to construct a coherent account of vision that explai...
Principles of Neural Information Theory
In this richly illustrated book, it is shown how Shannon's mathematical theory of information defines absolute limits on neural efficiency; limits which ultimately determine the neuroanatomical microstructure of the eye and brain. Written in an informal style this is an ideal introduction to cutting-edge research in neural information theory.
Unsupervised Learning
Since its founding in 1989 by Terrence Sejnowski, Neural Computation has become the leading journal in the field. Foundations of Neural Computation collects, by topic, the most significant papers that have appeared in the journal over the past nine years. This volume of Foundations of Neural Computation, on unsupervised learning algorithms, focuses on neural network learning algorithms that do not require an explicit teacher. The goal of unsupervised learning is to extract an efficient internal representation of the statistical structure implicit in the inputs. These algorithms provide insights into the development of the cerebral cortex and implicit learning in humans. They are also of interest to engineers working in areas such as computer vision and speech recognition who seek efficient representations of raw input data.
Artificial Life IV
This book brings together contributions to the Fourth Artificial Life Workshop, held at the Massachusetts Institute of Technology in the summer of 1994.
All the Light We Cannot See
*NOW A NETFLIX LIMITED SERIES—from producer and director Shawn Levy (Stranger Things) starring Mark Ruffalo, Hugh Laurie, and newcomer Aria Mia Loberti* Winner of the Pulitzer Prize and National Book Award finalist, the beloved instant New York Times bestseller and New York Times Book Review Top 10 Book about a blind French girl and a German boy whose paths collide in occupied France as both try to survive the devastation of World War II. Marie-Laure lives with her father in Paris near the Museum of Natural History where he works as the master of its thousands of locks. When she is six, Marie-Laure goes blind and her father builds a perfect miniature of their neighborhood so she can memori...
