'Layer networks' presentation slideshows

CHAPTER 11

CHAPTER 11. Back-Propagation. Objectives. A generalization of the LMS algorithm, called backpropagation , can be used to train multilayer networks . Backpropagation is an approximate steepest descent algorithm , in which the performance index is mean square error .

531 views • 39 slides

CS 391L: Machine Learning Neural Networks

CS 391L: Machine Learning Neural Networks. Raymond J. Mooney University of Texas at Austin. Neural Networks. Analogy to biological neural systems, the most robust learning systems we know. Attempt to understand natural biological systems through computational modeling.

602 views • 36 slides

394 views • 23 slides

GENI and the Challenges of Network Science

GENI and the Challenges of Network Science. Dr. Will E. Leland wel@research.telcordia.com 4 March 2008. GENI and Network Science Propositions of This Talk. Network science exists (almost true) GENI can contribute to Network Science Network Science can contribute to GENI

307 views • 15 slides

Kohei Shiomoto: shiomoto.kohei@lab.ntt.co.jp

Requirements for GMPLS-based multi-region and multi-layer networks draft-shiomoto-ccamp-gmpls-mrn-reqs-00.txt. Kohei Shiomoto: shiomoto.kohei@lab.ntt.co.jp Dimitri Papadimitriou: dimitri.papadimitriou@alcatel.be Jean-Louis Le Roux: jeanlouis.leroux@francetelecom.com

209 views • 11 slides