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Könyv Probabilistic Machine Learning Kevin P. Murphy

Probabilistic Machine Learning

Advanced Topics

Szerző: Kevin P. Murphy
Nyelv: Angol
Kötés: Kemény kötésű
Kiadó: MIT Press Ltd
Elérhetőség: Beszállítói készleten
Küldés 14-17 napon belül
59 244 Ft
An advanced book for researchers and graduate students working in machine learning and statistics wh...

Információk a könyvről

Szerző
Kevin P. Murphy
Nyelv
Angol
Kötés
Könyv - Kemény kötésű
Kiadva
2023
oldal
1360
EAN
9780262048439
Enbook ID
43194552
Kiadó
MIT Press Ltd
Súly
2020
Méretek
203 x 229

Teljes leírás

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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