Kevin P. Murphy 
Probabilistic Machine Learning [PDF ebook] 
Advanced Topics

Support
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, Deep Mind, 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
€380.51
méthodes de payement
Achetez cet ebook et obtenez-en 1 de plus GRATUITEMENT !
Langue Anglais ● Format PDF ● Pages 1360 ● ISBN 9780262375993 ● Maison d’édition The MIT Press ● Publié 2023 ● Téléchargeable 3 fois ● Devise EUR ● ID 9617369 ● Protection contre la copie Adobe DRM
Nécessite un lecteur de livre électronique compatible DRM

Plus d’ebooks du même auteur(s) / Éditeur

74 101 Ebooks dans cette catégorie