Dong Eui Chang – författare

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5 produkter

1. 

   Geometry, Mechanics, and Dynamics

   The Legacy of Jerry Marsden

   Av Dong Eui Chang, Darryl D. Holm m. fl.

   Inbunden, 2015

   1084 kr

   Lägg i varukorg

   This book illustrates the broad range of Jerry Marsden’s mathematical legacy in areas of geometry, mechanics, and dynamics, from very pure mathematics to very applied, but always with a geometric perspective. Each contribution develops its material from the viewpoint of geometric mechanics …

2. 

   Geometry, Mechanics, and Dynamics

   The Legacy of Jerry Marsden

   Av Tudor Ratiu, George Patrick m. fl.

   E-bok, 2015

   1459 kr

   Lägg i varukorg

   This book illustrates the broad range of Jerry Marsden’s mathematical legacy in areas of geometry, mechanics, and dynamics, from very pure mathematics to very applied, but always with a geometric perspective. Each contribution develops its material from the viewpoint of geometric mechanics …

3. 

   Geometry, Mechanics, and Dynamics

   The Legacy of Jerry Marsden

   Av Dong Eui Chang, Darryl D. Holm m. fl.

   Häftad, 2016

   1084 kr

   Lägg i varukorg

   This book illustrates the broad range of Jerry Marsden’s mathematical legacy in areas of geometry, mechanics, and dynamics, from very pure mathematics to very applied, but always with a geometric perspective. Each contribution develops its material from the viewpoint of geometric mechanics …

4. 

   Deep Neural Networks in a Mathematical Framework

   Av Anthony L. Caterini, Dong Eui Chang

   Häftad, 2018

   792 kr

   Lägg i varukorg

   This SpringerBrief describes how to build a rigorous end-to-end mathematical framework for deep neural networks. The authors provide tools to represent and describe neural networks, casting previous results in the field in a more natural light. In particular, the authors derive gradient descent …

5. 

   Deep Neural Networks in a Mathematical Framework

   Av Dong Eui Chang, Anthony L. Caterini

   E-bok, 2018

   1007 kr

   Lägg i varukorg

   This SpringerBrief describes how to build a rigorous end-to-end mathematical framework for deep neural networks. The authors provide tools to represent and describe neural networks, casting previous results in the field in a more natural light. In particular, the authors derive gradient descent …