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Handbook of Bayesian Deep Learning
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Handbook of Bayesian Deep Learning
This book provides a thorough introduction to Bayesian deep learning (BDL) BDL brings Bayesian reasoning to deep neural networks with the aim of making predictions useful for rational decision-making under uncertainty. Deep neural networks provide powerful and flexible models for this purpose, while Bayesian inference offers a principled framework for representing evidence and uncertainty and for assessing the decisions that predictions inform. The work develops a broad view of BDL, combining a principal route through Bayesian methods with foundational and mathematical aspects, questions of scalability to contemporary models and datasets, applications, and topical developments.
It places BDL within a wider perspective on machine learning: not as a specialised corner of the field, but as a framework for building powerful models while remaining formal about evidence, uncertainty, and the decisions that give predictions their purpose. Written by a large team of co-authors, the book covers a wide spectrum of methods in Bayesian deep learning, covering recent advances in the field. A self-contained introduction to BDL, the book is perfect for students, researchers and practitioners interested in learning about, adopting or advancing Bayesian deep learning.
| Medios de comunicación | Libros Hardcover Book (Libro con lomo y cubierta duros) |
| Pendiente de lanzamiento | 29 de marzo de 2027 |
| ISBN13 | 9781041066255 |
| Editores | Taylor & Francis Ltd |
| Páginas | 808 |
| Dimensiones | 150 × 220 × 20 mm · 1,24 kg (Peso (estimado)) |
