Gradient expectations : structure, origins, and synthesis of predictive neural networks / Keith L. Downing.

"This book investigates the notion that the brain's primary function is prediction and uses deep learning to explore how our brains manage prediction and how this can lead to improved AI systems"--

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Bibliographic Details
Main Author: Downing, Keith L. (Author)
Format: eBook
Language:English
Published: Cambridge, Massachusetts : The MIT Press, [2023]
Subjects:
Online Access:Click for online access

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100 1 |a Downing, Keith L.,  |e author. 
245 1 0 |a Gradient expectations :  |b structure, origins, and synthesis of predictive neural networks /  |c Keith L. Downing. 
246 3 0 |a Structure, origins, and synthesis of predictive neural networks 
264 1 |a Cambridge, Massachusetts :  |b The MIT Press,  |c [2023] 
300 |a 1 online resource (unpaged) :  |b illustrations (some color) 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
347 |a data file  |2 rda 
504 |a Includes bibliographical references and index. 
505 0 |a Conceptual foundations of prediction -- Biological foundations of prediction -- Neural energy networks -- Predictive coding -- Emergence of predictive networks -- Evolving artificial predictive networks. 
520 |a "This book investigates the notion that the brain's primary function is prediction and uses deep learning to explore how our brains manage prediction and how this can lead to improved AI systems"--  |c Provided by publisher. 
588 |a Description based on online resource; title from digital title page (viewed on July 12, 2023). 
650 0 |a Deep learning (Machine learning) 
650 0 |a Neural networks (Computer science) 
650 0 |a Conjugate gradient methods. 
650 7 |a Deep learning (Machine learning)  |2 fast 
650 7 |a Conjugate gradient methods  |2 fast 
650 7 |a Neural networks (Computer science)  |2 fast 
776 0 8 |i Print version:  |a Downing, Keith L.  |t Gradient expectations  |d [Cambridge, Massachusetts] : The MIT Press, [2023]  |z 9780262545617  |w (DLC) 2022037237 
856 4 0 |u https://doi.org/10.7551/mitpress/14723.001.0001  |y Click for online access 
903 |a MIT-D2O-2023 
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