Introduction to (Shallow) Neural networks

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During the talk the following was discussed
a. Logististic Regression
b. Feed Forward and backward propagation
c. Statistics of logistic Regression
d. Principles and properties of a shallow Network algorithm
e. Feed Forward and backward Propagation, Architecture,

About the speaker

Prof. Andreas Pester received his PhD from Kiev State University in 1979 and habilitated at University of Technology Dresden at 1984. He has more than 20 years of experience in eLearning in higher education, in strategic planning for using eLearning in higher education for internationalization and in platform evaluation. He‘s got more than 25 years of experience in teaching math and mathematical modelling, simulation technologies, remote engineering, online labs.
Currently he is a Professor of mathematics and mathematical modelling at the Carinthia University of Applied Sciences. He is an author of more than 75 publications.
  1. auacse
  2. Science
  3. Engineering
  4. Education
  5. Armenia
  6. AUA
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