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.
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
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
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
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.
Predicting Structured Data
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.
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.
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
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
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
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.
Predicting Structured Data
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.