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Problstc Mchn Lrng

Kevin P. Murphy

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Piracy Free

Express Delivery

Secure Transactions

Eco‑Conscious Packaging

Binding

Hardback

Number of Pages

1360

Age Group

All

Language

English

Book Summary

This graduate-level non-fiction text offers a rigorous, research-driven look at probabilistic machine learning. It centers on deep learning, Bayesian inference, generative models, and decision making under uncertainty, and is aimed at researchers and graduate students who want to connect cutting-edge methods with solid statistical foundations. The tone is educational, challenging, and inspiring.

The content is presented as a comprehensive, theory-to-application framework. Chapters weave formal modeling with practical demonstrations, and an online Python code accompaniment lets readers experiment with real datasets. Contributions from leading researchers and domain experts from organizations like Google, DeepMind, Amazon, and top universities provide perspectives on deep generative modeling, graphical models, reinforcement learning, and causal inference within a unified probabilistic framework.

Readers move through the material by following mathematical derivations and applying concepts through code, experiments, and concise case studies. The book stands out by placing deep learning in a broader statistical context, showing how probabilistic modeling, inference, and causal reasoning inform modern ML. Complex ideas are presented with clear progression from intuition to formal treatment, making advanced topics accessible to serious, motivated learners.

Key content elements: deep generative modeling, graphical models, Bayesian inference, reinforcement learning, causality, and latent variable models
Learning outcomes: apply probabilistic thinking to ML problems, design experiments, reason about uncertainty, and interpret results
Illustration and writing style: rigorous, theory-to-application approach with practical explanations and case studies
Interactive features: online Python code accompaniment with runnable examples for hands-on exploration
Coverage highlights: high-dimensional output generation (images, text, graphs) and training under distributional shifts

Upon finishing, readers gain a transferable framework for probabilistic machine learning, the ability to design and critique experiments under uncertainty, and the confidence to apply advanced techniques to real research questions. It builds curiosity, methodological rigor, and a deeper appreciation for the connections between deep learning and probabilistic inference, leaving a lasting impression as a foundational reference for advanced study and research.

Product Details

Author

Kevin P. Murphy

Publisher

Penguin Random House

Number of Pages

1360

Language

English

SKU

9780262048439

ISBN

9780262048439

Reading Age

All

Dimensions

21.3x5.5x23.6cm

Binding

Hardback

Problstc Mchn Lrng

₹ 13,200

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