menu
{ "item_title" : "Deep Learning Architectures", "item_author" : [" Ovidiu Calin "], "item_description" : "This book describes how neural networks operate from the mathematical point of view. As a result, neural networks can be interpreted both as function universal approximators and information processors. The book bridges the gap between ideas and concepts of neural networks, which are used nowadays at an intuitive level, and the precise modern mathematical language, presenting the best practices of the former and enjoying the robustness and elegance of the latter.This book can be used in a graduate course in deep learning, with the first few parts being accessible to senior undergraduates. In addition, the book will be of wide interest to machine learning researchers who are interested in a theoretical understanding of the subject.", "item_img_path" : "https://covers2.booksamillion.com/covers/bam/3/03/036/723/3030367231_b.jpg", "price_data" : { "retail_price" : "79.99", "online_price" : "79.99", "our_price" : "79.99", "club_price" : "79.99", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Deep Learning Architectures|Ovidiu Calin

Deep Learning Architectures : A Mathematical Approach

local_shippingShip to Me
In Stock.
FREE Shipping for Club Members help

Overview

This book describes how neural networks operate from the mathematical point of view. As a result, neural networks can be interpreted both as function universal approximators and information processors. The book bridges the gap between ideas and concepts of neural networks, which are used nowadays at an intuitive level, and the precise modern mathematical language, presenting the best practices of the former and enjoying the robustness and elegance of the latter.

This book can be used in a graduate course in deep learning, with the first few parts being accessible to senior undergraduates. In addition, the book will be of wide interest to machine learning researchers who are interested in a theoretical understanding of the subject.


This item is Non-Returnable

Details

  • ISBN-13: 9783030367237
  • ISBN-10: 3030367231
  • Publisher: Springer
  • Publish Date: February 2021
  • Dimensions: 9.21 x 6.14 x 1.57 inches
  • Shipping Weight: 2.4 pounds
  • Page Count: 760

Related Categories

You May Also Like...

    1

BAM Customer Reviews