Adaptive computation and machine learning series probabilistic machine learning
Rubriek: Textual/Printed/Reference Materials - Boek
Prijs: € 147.93
Verzending: Uiterlijk 10 oktober in huis
Inhoudsopgave:
Omschrijving:
An advanced book for researchers and graduate students working in machine learning and statistics who want to learn about deep learning, Bayesian inference, generative models, and decision making under uncertainty. An advanced counterpart to Probabilistic Machine Learning: An Introduction, this high-level textbook provides researchers and graduate students detailed coverage of cutting-edge topics in machine learning, including deep generative modeling, graphical models, Bayesian inference, reinforcement learning, and causality. This volume puts deep learning into a larger statistical context and unifies approaches based on deep learning with ones based on probabilistic modeling and inference. With contributions from top scientists and domain experts from places such as Google, DeepMind, Amazon, Purdue University, NYU, and the University of Washington, this rigorous book is essential to understanding the vital issues in machine learning. Covers generation of high dimensional outputs, such as images, text, and graphs Discusses methods for discovering insights about data, based on latent variable models Considers training and testing under different distributions Explores how to use probabilistic models and inference for causal inference and decision making Features online Python code accompaniment
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Product specificaties:
Taal: en
Bindwijze: Hardcover
Oorspronkelijke releasedatum: 15 augustus 2023
Aantal pagina's: 1360
Illustraties: Met illustraties
Hoofdauteur: Kevin P. Murphy
Hoofduitgeverij: Mit Press
Product breedte: 203 mm
Product lengte: 229 mm
Studieboek: Nee
Verpakking breedte: 203 mm
Verpakking lengte: 229 mm
Verpakkingsgewicht: 567 g
EAN: 9780262048439
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