Description: Please refer to the section BELOW (and NOT ABOVE) this line for the product details - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - Title:Deep Learning Architectures: A Mathematical ApproachISBN13:9783030367237ISBN10:3030367231Author:Calin, Ovidiu (Author)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 Binding:Paperback, PaperbackPublisher:SpringerPublication Date:2021-02-28Weight:2.4 lbsDimensions:Number of Pages:760Language:English
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Book Title: Deep Learning Architectures: A Mathematical Approach
Item Length: 10in
Item Width: 7in
Author: Ovidiu Calin
Publication Name: Deep Learning Architectures : a Mathematical Approach
Format: Trade Paperback
Language: English
Publisher: Springer International Publishing A&G
Publication Year: 2021
Series: Springer Series in the Data Sciences Ser.
Type: Textbook
Item Weight: 52.2 Oz
Number of Pages: xXx, 760 Pages
