Computers / Data Science / Neural Networks
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Understanding Deep Learning
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
Structure, Origins, and Synthesis of Predictive Neural Networks
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
An Economics Approach
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
Theory and Algorithms
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
Understanding the Power of Emergence
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
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
A Path to Safe Autonomy
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
Foundations and Learning Algorithms
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
Building Blocks for Complex Adaptive Systems
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.
Understanding Deep Learning
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
Structure, Origins, and Synthesis of Predictive Neural Networks
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
An Economics Approach
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
Theory and Algorithms
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
Understanding the Power of Emergence
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
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
A Path to Safe Autonomy
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
Foundations and Learning Algorithms
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
Building Blocks for Complex Adaptive Systems
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.