Anatomy and physiology, a key part of the core curriculum in surgical technology, is the central basic science course around which the knowledge of surgical technology revolves. However, most conventional A&P books do not cover the surgical aspects of anatomy and physiology that the Core Curriculum for Surgical Technology requires. Surgical Anatomy and Physiology for the Surgical Technologist provides the basic concepts of A&P and applies them to practical surgery. Throughout the book, examples show how the anatomy and physiology of a particular body system or organ relates to a surgical procedure. This resource includes case studies, review questions, key terms, objectives for each chapter, and information boxes that tie specific anatomical elements to surgical practice. This book meets the requirements of the current edition of the Core Curriculum for Surgical Technology. Important Notice: Media content referenced within the product description or the product text may not be available in the ebook version.
This new edition meets the requirements of the revised Core Curriculum for Surgical Technologists, 5th edition. It is written by surgical technologists for surgical technologists. The content focuses on the concepts and skill development (cognitive and procedural) required of surgical technologists in the operative environment. The text uses the A POSitive CARE approach to surgical problem solving that concentrates on the ability of the surgical technologist to predict the patient's and surgeon's needs through the intraoperative period. The goal is for the surgical technologist to apply this model in daily practice for maximum efficiency and effectiveness during the surgical procedure. The surgical procedures included in the text were selected for their instructive value and because the skills demonstrated can be applied to many other procedures.
A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach. Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach. The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package—PMTK (probabilistic modeling toolkit)—that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.
A detailed and up-to-date introduction to machine learning, presented through the unifying lens of probabilistic modeling and Bayesian decision theory. This book offers a detailed and up-to-date introduction to machine learning (including deep learning) through the unifying lens of probabilistic modeling and Bayesian decision theory. The book covers mathematical background (including linear algebra and optimization), basic supervised learning (including linear and logistic regression and deep neural networks), as well as more advanced topics (including transfer learning and unsupervised learning). End-of-chapter exercises allow students to apply what they have learned, and an appendix covers notation. Probabilistic Machine Learning grew out of the author’s 2012 book, Machine Learning: A Probabilistic Perspective. More than just a simple update, this is a completely new book that reflects the dramatic developments in the field since 2012, most notably deep learning. In addition, the new book is accompanied by online Python code, using libraries such as scikit-learn, JAX, PyTorch, and Tensorflow, which can be used to reproduce nearly all the figures; this code can be run inside a web browser using cloud-based notebooks, and provides a practical complement to the theoretical topics discussed in the book. This introductory text will be followed by a sequel that covers more advanced topics, taking the same probabilistic approach.
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