Read Books Online and Download eBooks, EPub, PDF, Mobi, Kindle, Text Full Free.
Visual Cortex And Deep Networks
Download Visual Cortex And Deep Networks full books in PDF, epub, and Kindle. Read online Visual Cortex And Deep Networks ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!
Book Synopsis Visual Cortex and Deep Networks by : Tomaso A. Poggio
Download or read book Visual Cortex and Deep Networks written by Tomaso A. Poggio and published by MIT Press. This book was released on 2016-09-23 with total page 135 pages. Available in PDF, EPUB and Kindle. Book excerpt: A mathematical framework that describes learning of invariant representations in the ventral stream, offering both theoretical development and applications. The ventral visual stream is believed to underlie object recognition in primates. Over the past fifty years, researchers have developed a series of quantitative models that are increasingly faithful to the biological architecture. Recently, deep learning convolution networks—which do not reflect several important features of the ventral stream architecture and physiology—have been trained with extremely large datasets, resulting in model neurons that mimic object recognition but do not explain the nature of the computations carried out in the ventral stream. This book develops a mathematical framework that describes learning of invariant representations of the ventral stream and is particularly relevant to deep convolutional learning networks. The authors propose a theory based on the hypothesis that the main computational goal of the ventral stream is to compute neural representations of images that are invariant to transformations commonly encountered in the visual environment and are learned from unsupervised experience. They describe a general theoretical framework of a computational theory of invariance (with details and proofs offered in appendixes) and then review the application of the theory to the feedforward path of the ventral stream in the primate visual cortex.
Book Synopsis Visual Cortex and Deep Networks by : Tomaso A. Poggio
Download or read book Visual Cortex and Deep Networks written by Tomaso A. Poggio and published by MIT Press. This book was released on 2016-09-23 with total page 135 pages. Available in PDF, EPUB and Kindle. Book excerpt: A mathematical framework that describes learning of invariant representations in the ventral stream, offering both theoretical development and applications. The ventral visual stream is believed to underlie object recognition in primates. Over the past fifty years, researchers have developed a series of quantitative models that are increasingly faithful to the biological architecture. Recently, deep learning convolution networks—which do not reflect several important features of the ventral stream architecture and physiology—have been trained with extremely large datasets, resulting in model neurons that mimic object recognition but do not explain the nature of the computations carried out in the ventral stream. This book develops a mathematical framework that describes learning of invariant representations of the ventral stream and is particularly relevant to deep convolutional learning networks. The authors propose a theory based on the hypothesis that the main computational goal of the ventral stream is to compute neural representations of images that are invariant to transformations commonly encountered in the visual environment and are learned from unsupervised experience. They describe a general theoretical framework of a computational theory of invariance (with details and proofs offered in appendixes) and then review the application of the theory to the feedforward path of the ventral stream in the primate visual cortex.
Book Synopsis Data-Driven Science and Engineering by : Steven L. Brunton
Download or read book Data-Driven Science and Engineering written by Steven L. Brunton and published by Cambridge University Press. This book was released on 2022-05-05 with total page 615 pages. Available in PDF, EPUB and Kindle. Book excerpt: A textbook covering data-science and machine learning methods for modelling and control in engineering and science, with Python and MATLAB®.
Book Synopsis Models of the Visual System by : George K. Hung
Download or read book Models of the Visual System written by George K. Hung and published by Springer Science & Business Media. This book was released on 2013-11-11 with total page 777 pages. Available in PDF, EPUB and Kindle. Book excerpt: Some of the best vision scientists in the world in their respective fields have contributed to chapters in this book. They have expertise in a wide variety of fields, including bioengineering, basic and clinical visual science, medicine, neurophysiology, optometry, and psychology. Their combined efforts have resulted in a high quality book that covers modeling and quantitative analysis of optical, neurosensory, oculomotor, perceptual and clinical systems. It includes only those techniques and models that have such fundamentally strong physiological, control system, and perceptual bases that they will serve as foundations for models and analysis techniques in the future. The book is aimed first towards seniors and beginning graduate students in biomedical engineering, neurophysiology, optometry, and psychology, who will gain a broad understanding of quantitative analysis of the visual system. In addition, it has sufficient depth in each area to be useful as an updated reference and tutorial for graduate and post-doctoral students, as well as general vision scientists.
Book Synopsis Strengthening Deep Neural Networks by : Katy Warr
Download or read book Strengthening Deep Neural Networks written by Katy Warr and published by "O'Reilly Media, Inc.". This book was released on 2019-07-03 with total page 233 pages. Available in PDF, EPUB and Kindle. Book excerpt: As deep neural networks (DNNs) become increasingly common in real-world applications, the potential to deliberately "fool" them with data that wouldn’t trick a human presents a new attack vector. This practical book examines real-world scenarios where DNNs—the algorithms intrinsic to much of AI—are used daily to process image, audio, and video data. Author Katy Warr considers attack motivations, the risks posed by this adversarial input, and methods for increasing AI robustness to these attacks. If you’re a data scientist developing DNN algorithms, a security architect interested in how to make AI systems more resilient to attack, or someone fascinated by the differences between artificial and biological perception, this book is for you. Delve into DNNs and discover how they could be tricked by adversarial input Investigate methods used to generate adversarial input capable of fooling DNNs Explore real-world scenarios and model the adversarial threat Evaluate neural network robustness; learn methods to increase resilience of AI systems to adversarial data Examine some ways in which AI might become better at mimicking human perception in years to come
Book Synopsis Unsupervised Learning by : Geoffrey Hinton
Download or read book Unsupervised Learning written by Geoffrey Hinton and published by MIT Press. This book was released on 1999-05-24 with total page 420 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Deep Learning in Visual Computing and Signal Processing by : Krishna Kant Singh
Download or read book Deep Learning in Visual Computing and Signal Processing written by Krishna Kant Singh and published by CRC Press. This book was released on 2022-10-20 with total page 289 pages. Available in PDF, EPUB and Kindle. Book excerpt: Covers both the fundamentals and the latest concepts in deep learning Presents some of the diverse applications of deep learning in visual computing and signal processing Includes over 90 figures and tables to elucidate the text
Book Synopsis Large-scale Neuronal Theories of the Brain by : Christof Koch
Download or read book Large-scale Neuronal Theories of the Brain written by Christof Koch and published by MIT Press. This book was released on 1994 with total page 376 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book originated at a small and informal workshop held in December of 1992 in Idyllwild, a relatively secluded resort village situated amid forests in the San Jacinto Mountains above Palm Springs in Southern California. Eighteen colleagues from a broad range of disciplines, including biophysics, electrophysiology, neuroanatomy, psychophysics, clinical studies, mathematics and computer vision, discussed 'Large Scale Models of the Brain, ' that is, theories and models that cover a broad range of phenomena, including early and late vision, various memory systems, selective attention, and the neuronal code underlying figure-ground segregation and awareness (for a brief summary of this meeting, see Stevens 1993). The bias in the selection of the speakers toward researchers in the area of visual perception reflects both the academic background of one of the organizers as well as the (relative) more mature status of vision compared with other modalities. This should not be surprising given the emphasis we humans place on'seeing' for orienting ourselves, as well as the intense scrutiny visual processes have received due to their obvious usefullness in military, industrial, and robotic applications. JMD.
Book Synopsis Explainable AI: Interpreting, Explaining and Visualizing Deep Learning by : Wojciech Samek
Download or read book Explainable AI: Interpreting, Explaining and Visualizing Deep Learning written by Wojciech Samek and published by Springer Nature. This book was released on 2019-09-10 with total page 435 pages. Available in PDF, EPUB and Kindle. Book excerpt: The development of “intelligent” systems that can take decisions and perform autonomously might lead to faster and more consistent decisions. A limiting factor for a broader adoption of AI technology is the inherent risks that come with giving up human control and oversight to “intelligent” machines. For sensitive tasks involving critical infrastructures and affecting human well-being or health, it is crucial to limit the possibility of improper, non-robust and unsafe decisions and actions. Before deploying an AI system, we see a strong need to validate its behavior, and thus establish guarantees that it will continue to perform as expected when deployed in a real-world environment. In pursuit of that objective, ways for humans to verify the agreement between the AI decision structure and their own ground-truth knowledge have been explored. Explainable AI (XAI) has developed as a subfield of AI, focused on exposing complex AI models to humans in a systematic and interpretable manner. The 22 chapters included in this book provide a timely snapshot of algorithms, theory, and applications of interpretable and explainable AI and AI techniques that have been proposed recently reflecting the current discourse in this field and providing directions of future development. The book is organized in six parts: towards AI transparency; methods for interpreting AI systems; explaining the decisions of AI systems; evaluating interpretability and explanations; applications of explainable AI; and software for explainable AI.
Book Synopsis Brain-Inspired Computing by : Katrin Amunts
Download or read book Brain-Inspired Computing written by Katrin Amunts and published by Springer Nature. This book was released on 2021-07-20 with total page 159 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book constitutes revised selected papers from the 4th International Workshop on Brain-Inspired Computing, BrainComp 2019, held in Cetraro, Italy, in July 2019. The 11 papers presented in this volume were carefully reviewed and selected for inclusion in this book. They deal with research on brain atlasing, multi-scale models and simulation, HPC and data infra-structures for neuroscience as well as artificial and natural neural architectures.
Book Synopsis Biological and Computer Vision by : Gabriel Kreiman
Download or read book Biological and Computer Vision written by Gabriel Kreiman and published by Cambridge University Press. This book was released on 2021-02-04 with total page 275 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces neural mechanisms of biological vision and how artificial intelligence algorithms learn to interpret images.
Book Synopsis Deep Learning to See by : Alessandro Betti
Download or read book Deep Learning to See written by Alessandro Betti and published by Springer Nature. This book was released on 2022-04-26 with total page 116 pages. Available in PDF, EPUB and Kindle. Book excerpt: The remarkable progress in computer vision over the last few years is, by and large, attributed to deep learning, fueled by the availability of huge sets of labeled data, and paired with the explosive growth of the GPU paradigm. While subscribing to this view, this work criticizes the supposed scientific progress in the field, and proposes the investigation of vision within the framework of information-based laws of nature. This work poses fundamental questions about vision that remain far from understood, leading the reader on a journey populated by novel challenges resonating with the foundations of machine learning. The central thesis proposed is that for a deeper understanding of visual computational processes, it is necessary to look beyond the applications of general purpose machine learning algorithms, and focus instead on appropriate learning theories that take into account the spatiotemporal nature of the visual signal. Serving to inspire and stimulate critical reflection and discussion, yet requiring no prior advanced technical knowledge, the text can naturally be paired with classic textbooks on computer vision to better frame the current state of the art, open problems, and novel potential solutions. As such, it will be of great benefit to graduate and advanced undergraduate students in computer science, computational neuroscience, physics, and other related disciplines.
Book Synopsis Principles of Neural Design by : Peter Sterling
Download or read book Principles of Neural Design written by Peter Sterling and published by MIT Press. This book was released on 2015-05-22 with total page 567 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neuroscience research has exploded, with more than fifty thousand neuroscientists applying increasingly advanced methods. A mountain of new facts and mechanisms has emerged. And yet a principled framework to organize this knowledge has been missing. In this book, Peter Sterling and Simon Laughlin, two leading neuroscientists, strive to fill this gap, outlining a set of organizing principles to explain the whys of neural design that allow the brain to compute so efficiently. Setting out to "reverse engineer" the brain -- disassembling it to understand it -- Sterling and Laughlin first consider why an animal should need a brain, tracing computational abilities from bacterium to protozoan to worm. They examine bigger brains and the advantages of "anticipatory regulation"; identify constraints on neural design and the need to "nanofy"; and demonstrate the routes to efficiency in an integrated molecular system, phototransduction. They show that the principles of neural design at finer scales and lower levels apply at larger scales and higher levels; describe neural wiring efficiency; and discuss learning as a principle of biological design that includes "save only what is needed." Sterling and Laughlin avoid speculation about how the brain might work and endeavor to make sense of what is already known. Their distinctive contribution is to gather a coherent set of basic rules and exemplify them across spatial and functional scales.
Book Synopsis Analysis of Visual Behavior by : David Ingle
Download or read book Analysis of Visual Behavior written by David Ingle and published by MIT Press (MA). This book was released on 1982 with total page 870 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Analysis of Visual Behavior" encompasses both theoretical and experimental research. It deals with the visual mechanisms of diverse vertebrate species from salamanders and toads to primates and humans and presents a stimulating interaction of the disciplines of anatomy, physiology, and behavioral science. Throughout, visual mechanisms are investigated from the point of view of the brain functioning at the organismic level, as opposed to the now more prevalent focus on the molecular and cellular levels. This approach allows researchers to deal with the patterns of visually guided behavior of animals in real-life situations.The twenty-six contributions in the book are divided among three sections: "Indentification and Localization Processes in Nonmammalian Vertebrates," introduced by David J. Ingle; "Visual Guidance of Motor Patterns: The Role of Visual Cortex and the Superior Colliculus," introduced by Melvyn A. Goodale; and "Recognition and Transfer Processes," introduced by Richard J. W. Mansfield.The editors are all university researchers in psychology: David J. Ingle at Brandeis, Melvyn A. Goodale at the University of Western Ontario, and Richard J. W. Mansfield at Harvard.
Book Synopsis The Deep Learning Revolution by : Terrence J. Sejnowski
Download or read book The Deep Learning Revolution written by Terrence J. Sejnowski and published by MIT Press. This book was released on 2018-10-23 with total page 354 pages. Available in PDF, EPUB and Kindle. Book excerpt: How deep learning—from Google Translate to driverless cars to personal cognitive assistants—is changing our lives and transforming every sector of the economy. The deep learning revolution has brought us driverless cars, the greatly improved Google Translate, fluent conversations with Siri and Alexa, and enormous profits from automated trading on the New York Stock Exchange. Deep learning networks can play poker better than professional poker players and defeat a world champion at Go. In this book, Terry Sejnowski explains how deep learning went from being an arcane academic field to a disruptive technology in the information economy. Sejnowski played an important role in the founding of deep learning, as one of a small group of researchers in the 1980s who challenged the prevailing logic-and-symbol based version of AI. The new version of AI Sejnowski and others developed, which became deep learning, is fueled instead by data. Deep networks learn from data in the same way that babies experience the world, starting with fresh eyes and gradually acquiring the skills needed to navigate novel environments. Learning algorithms extract information from raw data; information can be used to create knowledge; knowledge underlies understanding; understanding leads to wisdom. Someday a driverless car will know the road better than you do and drive with more skill; a deep learning network will diagnose your illness; a personal cognitive assistant will augment your puny human brain. It took nature many millions of years to evolve human intelligence; AI is on a trajectory measured in decades. Sejnowski prepares us for a deep learning future.
Book Synopsis Multivariate Statistical Machine Learning Methods for Genomic Prediction by : Osval Antonio Montesinos López
Download or read book Multivariate Statistical Machine Learning Methods for Genomic Prediction written by Osval Antonio Montesinos López and published by Springer Nature. This book was released on 2022-02-14 with total page 707 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is open access under a CC BY 4.0 license This open access book brings together the latest genome base prediction models currently being used by statisticians, breeders and data scientists. It provides an accessible way to understand the theory behind each statistical learning tool, the required pre-processing, the basics of model building, how to train statistical learning methods, the basic R scripts needed to implement each statistical learning tool, and the output of each tool. To do so, for each tool the book provides background theory, some elements of the R statistical software for its implementation, the conceptual underpinnings, and at least two illustrative examples with data from real-world genomic selection experiments. Lastly, worked-out examples help readers check their own comprehension.The book will greatly appeal to readers in plant (and animal) breeding, geneticists and statisticians, as it provides in a very accessible way the necessary theory, the appropriate R code, and illustrative examples for a complete understanding of each statistical learning tool. In addition, it weighs the advantages and disadvantages of each tool.
Book Synopsis Introduction to Functional Magnetic Resonance Imaging by : Richard B. Buxton
Download or read book Introduction to Functional Magnetic Resonance Imaging written by Richard B. Buxton and published by Cambridge University Press. This book was released on 2009-08-27 with total page 479 pages. Available in PDF, EPUB and Kindle. Book excerpt: Functional Magnetic Resonance Imaging (fMRI) has become a standard tool for mapping the working brain's activation patterns, both in health and in disease. It is an interdisciplinary field and crosses the borders of neuroscience, psychology, psychiatry, radiology, mathematics, physics and engineering. Developments in techniques, procedures and our understanding of this field are expanding rapidly. In this second edition of Introduction to Functional Magnetic Resonance Imaging, Richard Buxton – a leading authority on fMRI – provides an invaluable guide to how fMRI works, from introducing the basic ideas and principles to the underlying physics and physiology. He covers the relationship between fMRI and other imaging techniques and includes a guide to the statistical analysis of fMRI data. This book will be useful both to the experienced radiographer, and the clinician or researcher with no previous knowledge of the technology.
