Neural Web (Human++ Book 3) PDF Download
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Author: Dima Zales
Publisher: Mozaika LLC
ISBN: 1631423045
Category : Fiction
Languages : en
Pages : 303
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Book Description
Author: Dima Zales
Publisher: Mozaika LLC
ISBN: 1631423045
Category : Fiction
Languages : en
Pages : 303
Get Book Here
Book Description
Author: Susan Weinschenk
Publisher: Peachpit Press
ISBN: 0321648765
Category : Computers
Languages : en
Pages : 169
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Book Description
“While you’re reading Neuro Web Design, you’ll probably find yourself thinking ‘I already knew that…’ a lot. But when you’re finished, you’ll discover that your ability to create effective web sites has mysteriously improved. A brilliant idea for a book, and very nicely done.” – Steve Krug, author of Don’t Make Me Think! A Common Sense Approach to Web Usability Why do people decide to buy a product online? Register at your Web site? Trust the information you provide? Neuro Web Design applies the research on motivation, decision making, and neuroscience to the design of Web sites. You will learn the unconscious reasons for people’s actions, how emotions affect decisions, and how to apply the principles of persuasion to design Web sites that encourage users to click. Neuro Web Design employs “neuro-marketing” concepts, which are at the intersection of psychology and user experience. It’s scientific, yet you’ll find it accessible, easy to read, and easy to understand. By applying the concepts and examples in this book, you’ll be able to dramatically increase the effectiveness and conversion rates of your own Web site.
Author: Wulfram Gerstner
Publisher: Cambridge University Press
ISBN: 1107060834
Category : Computers
Languages : en
Pages : 591
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Book Description
This solid introduction uses the principles of physics and the tools of mathematics to approach fundamental questions of neuroscience.
Author: Jeff Heaton
Publisher: Createspace Independent Publishing Platform
ISBN: 9781505714340
Category : Algorithms
Languages : en
Pages : 0
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Book Description
« Artifical Intelligence for Humans is a book series meant to teach AI to those readers who lack an extensive mathematical background. The reader only needs knowledge of basic college algebra and computer programming. Additional topics are thoroughly explained. Every chapter also includes a programming example. Examples are currently provided in Java, C#, and Python. Other languages are planned. »--
Author: Melanie Mitchell
Publisher: Farrar, Straus and Giroux
ISBN: 0374715238
Category : Computers
Languages : en
Pages : 336
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Book Description
Melanie Mitchell separates science fact from science fiction in this sweeping examination of the current state of AI and how it is remaking our world No recent scientific enterprise has proved as alluring, terrifying, and filled with extravagant promise and frustrating setbacks as artificial intelligence. The award-winning author Melanie Mitchell, a leading computer scientist, now reveals AI’s turbulent history and the recent spate of apparent successes, grand hopes, and emerging fears surrounding it. In Artificial Intelligence, Mitchell turns to the most urgent questions concerning AI today: How intelligent—really—are the best AI programs? How do they work? What can they actually do, and when do they fail? How humanlike do we expect them to become, and how soon do we need to worry about them surpassing us? Along the way, she introduces the dominant models of modern AI and machine learning, describing cutting-edge AI programs, their human inventors, and the historical lines of thought underpinning recent achievements. She meets with fellow experts such as Douglas Hofstadter, the cognitive scientist and Pulitzer Prize–winning author of the modern classic Gödel, Escher, Bach, who explains why he is “terrified” about the future of AI. She explores the profound disconnect between the hype and the actual achievements in AI, providing a clear sense of what the field has accomplished and how much further it has to go. Interweaving stories about the science of AI and the people behind it, Artificial Intelligence brims with clear-sighted, captivating, and accessible accounts of the most interesting and provocative modern work in the field, flavored with Mitchell’s humor and personal observations. This frank, lively book is an indispensable guide to understanding today’s AI, its quest for “human-level” intelligence, and its impact on the future for us all.
Author: Ian Goodfellow
Publisher: MIT Press
ISBN: 0262337371
Category : Computers
Languages : en
Pages : 801
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Book Description
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.
Author: H. Richard Winn
Publisher: Elsevier Health Sciences
ISBN: 0323674992
Category : Medical
Languages : en
Pages : 7439
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Book Description
Widely regarded as the definitive reference in the field, Youmans and Winn Neurological Surgery offers unparalleled, multimedia coverage of the entirety of this complex specialty. Fully updated to reflect recent advances in the basic and clinical neurosciences, the 8th Edition covers everything you need to know about functional and restorative neurosurgery, deep brain stimulation, stem cell biology, radiological and nuclear imaging, and neuro-oncology, as well as minimally invasive surgeries in spine and peripheral nerve surgery, and endoscopic and other approaches for cranial procedures and cerebrovascular diseases. In four comprehensive volumes, Dr. H. Richard Winn and his expert team of editors and authors provide updated content, a significantly expanded video library, and hundreds of new video lectures that help you master new procedures, new technologies, and essential anatomic knowledge in neurosurgery. - Discusses current topics such as diffusion tensor imaging, brain and spine robotic surgery, augmented reality as an aid in neurosurgery, AI and big data in neurosurgery, and neuroimaging in stereotactic functional neurosurgery. - 55 new chapters provide cutting-edge information on Surgical Anatomy of the Spine, Precision Medicine in Neurosurgery, The Geriatric Patient, Neuroanesthesia During Pregnancy, Laser Interstitial Thermal Therapy for Epilepsy, Fetal Surgery for Myelomeningocele, Rehabilitation of Acute Spinal Cord Injury, Surgical Considerations for Patients with Polytrauma, Endovascular Approaches to Intracranial Aneurysms, and much more. - Hundreds of all-new video lectures clarify key concepts in techniques, cases, and surgical management and evaluation. Notable lecture videos include multiple videos on Thalamotomy for Focal Hand Dystonia and a video to accompany a new chapter on the Basic Science of Brain Metastases. - An extensive video library contains stunning anatomy videos and videos demonstrating intraoperative procedures with more than 800 videos in all. - Each clinical section contains chapters on technology specific to a clinical area. - Each section contains a chapter providing an overview from experienced Section Editors, including a report on ongoing controversies within that subspecialty. - Enhanced eBook version included with purchase. Your enhanced eBook allows you to access all of the text, figures, and references from the book on a variety of devices.
Author: Alfredo Quinones-Hinojosa
Publisher: Elsevier Health Sciences
ISBN: 0323415199
Category : Medical
Languages : en
Pages : 2913
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Book Description
Schmidek and Sweet has been an indispensable reference for neurosurgery training and practice for nearly 50 years, and the 7th Edition of Operative Neurosurgical Techniques continues this tradition of excellence. A new editorial board led by editor-in-chief Dr. Alfredo Quinones-Hinojosa, along with more than 330 internationally acclaimed contributors, ensures that readers stay fully up to date with rapid changes in the field. New chapters, surgical videos, and quick-reference features throughout make this edition a must-have resource for expert procedural guidance for today's practitioners. - Discusses indications, operative techniques, complications, and results for nearly every routine and specialized procedure for brain, spinal, and peripheral nerve problems in adult patients. - Covers the latest techniques and knowledge in deep brain stimulation for epilepsy, movement disorders, dystonia, and psychiatric disorders; surgical management of blast injuries; invasive electrophysiology in functional neurosurgery; and interventional management of cerebral aneurysms and arterio-venous malformations. - Includes new chapters on bypass techniques in vascular disease, previously coiled aneurysms, CSF diversion procedures, surgical management of posterior fossa cystic and membranous obstruction, laser-ablation techniques, and brain stem tumors. - Explores hot topics such as wide-awake surgery and ventriculo-peritoneal, ventriculoatrial and ventriculo-pleural shunts. - Provides detailed visual guidance with more than 1,600 full-color illustrations and 50 procedural videos. - Contains quick-reference boxes with surgical pearls and complications. - Enhanced eBook version included with purchase. Your enhanced eBook allows you to access all of the text, figures, and references from the book on a variety of devices.
Author: James A. Anderson
Publisher: MIT Press
ISBN: 9780262511117
Category : Medical
Languages : en
Pages : 452
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Book Description
Surprising tales from the scientists who first learned how to use computers to understand the workings of the human brain. Since World War II, a group of scientists has been attempting to understand the human nervous system and to build computer systems that emulate the brain's abilities. Many of the early workers in this field of neural networks came from cybernetics; others came from neuroscience, physics, electrical engineering, mathematics, psychology, even economics. In this collection of interviews, those who helped to shape the field share their childhood memories, their influences, how they became interested in neural networks, and what they see as its future. The subjects tell stories that have been told, referred to, whispered about, and imagined throughout the history of the field. Together, the interviews form a Rashomon-like web of reality. Some of the mythic people responsible for the foundations of modern brain theory and cybernetics, such as Norbert Wiener, Warren McCulloch, and Frank Rosenblatt, appear prominently in the recollections. The interviewees agree about some things and disagree about more. Together, they tell the story of how science is actually done, including the false starts, and the Darwinian struggle for jobs, resources, and reputation. Although some of the interviews contain technical material, there is no actual mathematics in the book. Contributors James A. Anderson, Michael Arbib, Gail Carpenter, Leon Cooper, Jack Cowan, Walter Freeman, Stephen Grossberg, Robert Hecht-Neilsen, Geoffrey Hinton, Teuvo Kohonen, Bart Kosko, Jerome Lettvin, Carver Mead, David Rumelhart, Terry Sejnowski, Paul Werbos, Bernard Widrow
Author: Terrence J. Sejnowski
Publisher: MIT Press
ISBN: 026203803X
Category : Computers
Languages : en
Pages : 354
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Book Description
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.
