menu
{ "item_title" : "Mathematical Theory of Deep Learning", "item_author" : [" Philipp Petersen", "Jakob Zech "], "item_description" : "This open access book offers a comprehensive introduction to a wide variety of topics in the mathematical theory of deep learning. These include questions pertaining to the prowess of deep neural networks, the surprising effectiveness of current learning algorithms, and the puzzling capability of huge neural networks to make accurate predictions on unseen data. The text focuses on rigorous but accessible results, and provides deep insights into the reasons why this field has been so successful. The book proves useful for mathematicians with a working knowledge of probability theory, linear algebra, and analysis. ", "item_img_path" : "https://covers1.booksamillion.com/covers/bam/3/03/239/921/3032399211_b.jpg", "price_data" : { "retail_price" : "59.99", "online_price" : "59.99", "our_price" : "59.99", "club_price" : "59.99", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Mathematical Theory of Deep Learning|Philipp Petersen

Mathematical Theory of Deep Learning

PRE-ORDER NOW:
local_shippingShip to Me
Preorder. This item will be available on March 13, 2027 .
FREE Shipping for Club Members help

Overview

This open access book offers a comprehensive introduction to a wide variety of topics in the mathematical theory of deep learning. These include questions pertaining to the prowess of deep neural networks, the surprising effectiveness of current learning algorithms, and the puzzling capability of huge neural networks to make accurate predictions on unseen data. The text focuses on rigorous but accessible results, and provides deep insights into the reasons why this field has been so successful. The book proves useful for mathematicians with a working knowledge of probability theory, linear algebra, and analysis.

This item is Non-Returnable

Details

  • ISBN-13: 9783032399212
  • ISBN-10: 3032399211
  • Publisher: Springer
  • Publish Date: March 2027

Related Categories

You May Also Like...

    1

BAM Customer Reviews