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Optimization for Machine Learning (Neural Information Processing series) by Suvrit Sra; Sebastian Nowozin; Stephen J. Wright; Francis Bach; Rodolphe Jenatton; Julien Mairal; Guillaume Obozinski; Martin Andersen; Joachim Dahl; Zhang Liu; Lieven Vandenberghe; Dimitri Bertsekas; Anatoli Juditsky; Arkadi Nemirovski; Vojtech Franc; Sren Sonnenburg; Thomas Werner; David Sontag; Amir Globerson; Tommi Jaakkola; Ryota Tomioka; Taiji Suzuki; Masashi Sugiyama; Elad Hazan; Mark Schmidt; Dongmin Kim; Jacek Gondzio; Lon Bottou; Olivier Bousquet; Constantine Caramanis; Shie Mannor; Huan Xu; Nicolas Le Roux; Yoshua Bengio; Andrew FitzGibbon; Jean-Yves Audibert; Sbsatien Bubeck; Remi Munos; Katya Scheinberg; Shiqian Ma; Vijay Krishnamurthy; Selin Damla Ahipasaoglu; Alexandre D'Aspremont is a nonfiction available to read on EtoBox.

What is Optimization for Machine Learning (Neural Information Processing series) about?

An up-to-date account of the interplay between optimization and machine learning, accessible to students and researchers in both communities. The interplay between optimization and machine learning is one of the most important developments in modern computational science. Optimization formulations and methods are proving to be vital in designing algorithms to extract essential knowledge from huge volumes of data. Machine learning, however, is not simply a consumer of optimization technology but a rapidly evolving field that is itself generating new optimization ideas. This book captures the state of the art of the interaction between optimization and machine learning in a way that is accessible to researchers in both fields. Optimization approaches have enjoyed prominence in machine learning because of their wide applicability and attractive theoretical properties. The increasing complexity, size, and variety of today's machine learning models call for the reassessment of existing assumptions. This book starts the process of reassessment. It describes the resurgence in novel contexts of established frameworks such as first-order methods, stochastic approximations, convex relaxation

Who reads Optimization for Machine Learning (Neural Information Processing series)?

Author

Suvrit Sra; Sebastian Nowozin; Stephen J. Wright; Francis Bach; Rodolphe Jenatton; Julien Mairal; Guillaume Obozinski; Martin Andersen; Joachim Dahl; Zhang Liu; Lieven Vandenberghe; Dimitri Bertsekas; Anatoli Juditsky; Arkadi Nemirovski; Vojtech Franc; Sren Sonnenburg; Thomas Werner; David Sontag; Amir Globerson; Tommi Jaakkola; Ryota Tomioka; Taiji Suzuki; Masashi Sugiyama; Elad Hazan; Mark Schmidt; Dongmin Kim; Jacek Gondzio; Lon Bottou; Olivier Bousquet; Constantine Caramanis; Shie Mannor; Huan Xu; Nicolas Le Roux; Yoshua Bengio; Andrew FitzGibbon; Jean-Yves Audibert; Sbsatien Bubeck; Remi Munos; Katya Scheinberg; Shiqian Ma; Vijay Krishnamurthy; Selin Damla Ahipasaoglu; Alexandre D'Aspremont

Publisher

The MIT Press

Published

2012

Language

EN

ISBN

9780262537766

Category

nonfiction

Subjects

Computer Science, Science, Engineering