Most jobs need reasoning—drawing conclusions based on available data—by a person or an automated system. This book's framework of probabilistic graphical models provides a generic approach to this problem. The method is model-based, allowing for the creation of interpretable models that may then be changed by reasoning algorithms. These models can also be trained automatically from data, which means they can be utilised in situations when manually building a model is difficult or impossible. Because uncertainty is an unavoidable part of most real-world applications, the book focuses on probabilistic models, which make uncertainty explicit and enable more accurate models.
Most speech-language pathologists will, at some point, be frustrated by their clients' attempts to elicit novel speech sound behaviours. This is particularly true when a client lacks a target...
Hands-On Machine Learning with Scikit-Learn and TensorFlow: "The breadth of topics the book covers is amazing for just 100 pages. Burkov doesn't hesita
The theory and applications of structural analysis as they relate to trusses, beams, and frames are covered in Structural Analysis. In order to prepare for professional practise, the reader-friendly,...
Drawing on examples from famous media and journals, creator Beth Morling conjures up a love of her problem with the aid of using emphasizing its relevance. Yes, college students discover ways to...
Award-winning and highly lauded, Psychotherapy for the Advanced Practice Psychiatric Nurse is a how-to compendium of evidence-based approaches for both new and experienced advanced...
This book, written by experts in the field of psychiatric-mental health nurse practitioner practise, serves as a clinical reference tool as well as a study guide for certification exams. This book...
