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Computers / Data Science / Neural Networks

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Gradient Expectations

Gradient Expectations

Gradient Expectations

Structure, Origins, and Synthesis of Predictive Neural Networks

by Keith L. Downing

ISBN: 9780262545617

Publisher: The MIT Press

Pub Date: July 18, 2023

An insightful investigation into the mechanisms underlying the predictive functions of neural networks—and their ability to chart a new path for AI.
Networks

Networks

Networks

An Economics Approach

by Sanjeev Goyal

ISBN: 9780262048033

Publisher: The MIT Press

Pub Date: April 18, 2023

An accessible and comprehensive overview of the economic theory and the realities of networks written by a pioneering economics researcher.
Learning Kernel Classifiers

Learning Kernel Classifiers

Learning Kernel Classifiers

Theory and Algorithms

by Ralf Herbrich

ISBN: 9780262546591

Publisher: The MIT Press

Pub Date: November 1, 2022

An overview of the theory and application of kernel classification methods.
The Cortex and the Critical Point

The Cortex and the Critical Point

The Cortex and the Critical Point

Understanding the Power of Emergence

by John M. Beggs

ISBN: 9780262544030

Publisher: The MIT Press

Pub Date: August 30, 2022

How the cerebral cortex operates near a critical phase transition point for optimum performance.
Algorithms for Decision Making

Algorithms for Decision Making

Algorithms for Decision Making

by Mykel J. Kochenderfer, Tim A. Wheeler and Kyle H. Wray

ISBN: 9780262047012

Publisher: The MIT Press

Pub Date: August 16, 2022

A broad introduction to algorithms for decision making under uncertainty, introducing the underlying mathematical problem formulations and the algorithms for solving them.
Verifying Cyber-Physical Systems

Verifying Cyber-Physical Systems

Verifying Cyber-Physical Systems

A Path to Safe Autonomy

by Sayan Mitra

ISBN: 9780262044806

Publisher: The MIT Press

Pub Date: February 16, 2021

A graduate-level textbook that presents a unified mathematical framework for modeling and analyzing cyber-physical systems, with a strong focus on verification.
Elements of Causal Inference

Elements of Causal Inference

Elements of Causal Inference

Foundations and Learning Algorithms

by Jonas Peters, Dominik Janzing and Bernhard Schölkopf

ISBN: 9780262037310

Publisher: The MIT Press

Pub Date: November 29, 2017

A concise and self-contained introduction to causal inference, increasingly important in data science and machine learning.
Signals and Boundaries

Signals and Boundaries

Signals and Boundaries

Building Blocks for Complex Adaptive Systems

by John H. Holland

ISBN: 9780262525930

Publisher: The MIT Press

Pub Date: January 10, 2014

An overarching framework for comparing and steering complex adaptive systems is developed through understanding the mechanisms that generate their intricate signal/boundary hierarchies.
Predicting Structured Data

Predicting Structured Data

Predicting Structured Data

Edited by Gökhan BakIr, Thomas Hofmann, Bernhard Schölkopf, Alexander J. Smola, Ben Taskar and S.V.N Vishwanathan

ISBN: 9780262528047

Publisher: The MIT Press

Pub Date: July 27, 2007

State-of-the-art algorithms and theory in a novel domain of machine learning, prediction when the output has structure.
Toward Brain-Computer Interfacing

Toward Brain-Computer Interfacing

Toward Brain-Computer Interfacing

Edited by Guido Dornhege, José del R. Millán, Thilo Hinterberger, Dennis J. McFarland and Klaus-Robert Müller

ISBN: 9780262527880

Publisher: The MIT Press

Pub Date: July 20, 2007

The latest research in the development of technologies that will allow humans to communicate, using brain signals only, with computers, wheelchairs, prostheses, and other devices.
‹123›

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