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        Computers / Data Science / Neural Networks

        Showing results 1-10 of 24

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        Understanding Deep Learning

        Understanding Deep Learning

        Understanding Deep Learning

        by Simon J. D. Prince

        ISBN: 9780262048644

        Publisher: The MIT Press

        Pub Date: December 5, 2023

        An authoritative, accessible, and up-to-date treatment of deep learning that strikes a pragmatic middle ground between theory and practice.
        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.
        ‹123›

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