ISBN: 9780262048439 - probabilistic machine learning: advanced topics (adaptive computation and machine learning series) (35 resultados)

hardcover. Condición: Very Good. Very Good Condition - May show some limited signs of wear and may have a remainder mark. Pages and dust cover are intact and not marred by notes or highlighting.

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Condición: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.

Condición: Brand New. New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service.

Condición: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.

HRD. Condición: New. New Book. Shipped from UK. Established seller since 2000.

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Hardcover. Condición: new. Hardcover. 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 distributionsExplores how to use probabilistic models and inference for causal inference and decision makingFeatures online Python code accompaniment "An advanced book for researchers and graduate students working in machine learning and statistics that reflects the influence of deep learning"-- Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

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Hardcover. Condición: Brand New. 1352 pages. 9.50x8.50x2.25 inches. In Stock.

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Hardcover. Condición: New. Brand new book, sourced directly from publisher. Dispatch time is 24-48 hours from our warehouse. Book will be sent in robust, secure packaging to ensure it reaches you securely.

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Condición: New. In English.

Condición: New. Kevin P. Murphy is a Research Scientist at Google in Mountain View, California, where he works on artificial intelligence, machine learning, and Bayesian modeling.An advanced book for researchers and graduate students working in machine learning and .

Condición: New. 2023. Hardcover. . . . . .

Condición: New. 2023. Hardcover. . . . . . Books ship from the US and Ireland.

Buch. Condición: Neu. Neuware - 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.…