Probabilistic Machine Learning at Meripustak

Probabilistic Machine Learning

Books from same Author: Kevin P Murphy

Books from same Publisher: Mit Press

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  • General Information  
    Author(s)Kevin P Murphy
    PublisherMit Press
    ISBN9780262048439
    Pages1360
    BindingHardcover
    LanguageEnglish
    Publish YearAugust 2023

    Description

    Mit Press Probabilistic Machine Learning by Kevin P Murphy

    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 graphsDiscusses methods for discovering insights about data, based on latent variable modelsConsiders training and testing under different distributionsExplores how to use probabilistic models and inference for causal inference and decision makingFeatures online Python code accompaniment