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Probabilistic Machine Learning Advanced Topics Draft April 1st 2023 Kevin P Murphy

  • SKU: BELL-51228290
Probabilistic Machine Learning Advanced Topics Draft April 1st 2023 Kevin P Murphy
$ 31.00 $ 45.00 (-31%)

4.4

52 reviews

Probabilistic Machine Learning Advanced Topics Draft April 1st 2023 Kevin P Murphy instant download after payment.

Publisher: The MIT Press
File Extension: PDF
File size: 38.91 MB
Pages: 1360
Author: Kevin P. Murphy
ISBN: 9780262048439, 9780262375993, 0262048434, 0262375990
Language: English
Year: 2023

Product desciption

Probabilistic Machine Learning Advanced Topics Draft April 1st 2023 Kevin P Murphy by Kevin P. Murphy 9780262048439, 9780262375993, 0262048434, 0262375990 instant download after payment.

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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