Description

Book Synopsis
Introduction.- Background.- Conventional Statistical Learning Theory.- Difficulty of Conventional Statistical Learning Theory.- Developing Deep Learning Theory.- Generalization Bounds on Hypothesis Complexity.- Interplay of Optimization, Bayesian Inference, and Generalization.- Geometrical Properties of Loss Surface.- The Role of Over-parametrization.- Rising Concerns in Ethics and Security.- Privacy Preservation.- Fairness Protection.- Algorithmic Robustness.

Foundations of Deep Learning

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    A Hardback by Fengxiang He

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      Publisher: Springer
      Publication Date: 3/17/2025
      ISBN13: 9789811682322, 978-9811682322
      ISBN10: 9811682321
      Also in:
      Artificial intelligence (AI)

      Description

      Book Synopsis
      Introduction.- Background.- Conventional Statistical Learning Theory.- Difficulty of Conventional Statistical Learning Theory.- Developing Deep Learning Theory.- Generalization Bounds on Hypothesis Complexity.- Interplay of Optimization, Bayesian Inference, and Generalization.- Geometrical Properties of Loss Surface.- The Role of Over-parametrization.- Rising Concerns in Ethics and Security.- Privacy Preservation.- Fairness Protection.- Algorithmic Robustness.

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