你好! Shipping to Taiwan with premium packaging for just NT$300 

Ship to
Taiwan
0

My Account

  • argentina
  • chile
  • colombia
  • españa
  • méxico
  • perú
  • estados unidos
  • internacional

menu

Select your country

Americas

Europe

Rest of the world

portada Mathematical Foundations of Deep Learning Models and Algorithms
Type
Physical Book
Publisher
Author
Collection
Graduate Studies in Mathematics
Year
2026
Language
English
Pages
550
Format
Paperback
Dimensions
25.7x18x3.1 cm
ISBN13
9781470483999

Mathematical Foundations of Deep Learning Models and Algorithms

Justin Sirignano;Richard B. Sowers;Konstantinos Spiliopoulos (Author) · American Mathematical Society · Paperback

Mathematical Foundations of Deep Learning Models and Algorithms - Justin Sirignano;Richard B. Sowers;Konstantinos Spiliopoulos

New Book Imported to Taiwan
Delivery: 11 Nov - 25 Nov Shipping: 10 to 15 business days.
NT$ 4,236
NT$ 4,236

Choose the list to add your product or create one New List

Go to My Wishlists
Calculate shipping cost

Synopsis "Mathematical Foundations of Deep Learning Models and Algorithms"

Deep learning uses multi-layer neural networks to model complex data patterns. Large models-with millions or even billions of parameters-are trained on massive datasets. This approach has produced revolutionary advances in image, text, and speech recognition and also has potential applications in a range of other fields such as engineering, finance, mathematics, and medicine. This book provides an introduction to the mathematical theory underpinning the recent advances in deep learning. Detailed derivations as well as mathematical proofs are presented for many of the models and optimization methods which are commonly used in machine learning and deep learning. Applications, code, and practical approaches to training models are also included. The book is designed for advanced undergraduates, graduate students, practitioners, and researchers. Divided into two parts, it begins with mathematical foundations before tackling advanced topics in approximation, optimization, and neural network training. Part 1 is written for a general audience, including students in mathematics, statistics, computer science, data science, or engineering, while select chapters in Part 2 present more advanced mathematical theory requiring familiarity with analysis, probability, and stochastic processes. Together, they form an ideal foundation for an introductory course on the mathematics of deep learning. Thoughtfully designed exercises and a companion website with code examples enhance both theoretical understanding and practical skills, preparing readers to engage more deeply with this fast-evolving field.

Customers reviews

More customer reviews
  • 0% (0)
  • 0% (0)
  • 0% (0)
  • 0% (0)
  • 0% (0)

Frequently Asked Questions about the Book

Is the book original?

All books in our catalog are Original.

In what language is the book written?

The book is written in English.

What is the format of this book?

The binding of this edition is Paperback.

Questions and Answers about the Book

Do you have a question about the book? Login to be able to add your own question.

Opinions about Bookdelivery

More customer reviews