Mathematical foundations of deep learning models and algorithms / Konstantinos Spiliopoulos, Richard Sowers, Justin Sirignano.

Author/creator Spiliopoulos, Konstantinos, 1980- author.
Other author Sowers, R. B. (Richard Bucher), 1965- author.
Other author Sirignano, Justin (Justin Anthony), 1988- author.
Format Book
PublicationProvidence, Rhode Island : American Mathematical Society, [2025]
Descriptionpages cm.
Subjects
Deep learning (Machine learning)--Mathematical models.
Neural networks (Computer science)--Mathematical models.
Regression analysis.
Convergence.
Kernel functions.
Apprentissage profond--Modèles mathématiques.
Réseaux neuronaux (Informatique)--Modèles mathématiques.
Analyse de régression.
Convergence (Mathématiques)
Noyaux (Mathématiques)

SeriesGraduate studies in mathematics, 1065-7339 ; volume 252
Graduate studies in mathematics ; volume 252. ^A347883
Contents Linear regression -- Logistic regression -- From perceptron to kernels to neural networks -- Feed forward neural networks -- Backpropagation -- Basics on stochastic gradient descent -- Stochastic gradient descent for multi-layer networks -- Regularization and dropout -- Batch normalization -- Training, validation, and testing -- Feature importance -- Recurrent neural networks and sequential data -- Convolution neural networks -- Variational inference and generative models -- Universal approximation theorems -- Convergence analysis of gradient descent -- Convergence analysis of stochastic gradient descent -- The neural tangent kernel regime -- Optimization in feature learning regime : mean field scaling -- Reinforcement learning -- Neural differential equations -- Distributed training -- Automatic differentiation.
Bibliography noteIncludes bibliographical references and index.
LCCN 2025030859
ISBN9781470481087 hardcover
ISBN1470481081
ISBN9781470483999 paperback
ISBN1470483998
ISBNebook

Availability

Library Location Call Number Status Item Actions
Joyner General Stacks Q325.73 .S65 2025 ✔ Available Place Hold