Sinonasal Surgery is part of the popular series Surgical Techniques in Otolaryngology – Head & Neck Surgery. This volume is a practical guide for otolaryngologists providing an overview of the most common, and some of the more advanced procedures performed in nose and sinus surgery. Edited by a team of recognised US based otolaryngologists, this authoritative atlas is unique in its scope. The book first outlines the basic principles of rhinologic practice, followed by sections on Inflammatory Sinus Disease, Adjunctive Surgical Procedures, Nasal Tumours and Endoscopic Skull Base Surgery. Each chapter presents an evidence-based approach to the development of each surgical procedure, and a description of the techniques, with intraoperative photographs and discussion on the benefits and pitfalls of each one. Additional descriptions of newer surgical methods such as balloon dilation of the sinuses, endoscopic transodontoid approaches, and nasopharyngectomy, make Sinonasal Surgery an up-to-date, essential text for the practising otolaryngologist. Key points Edited by US-based team of ENT specialists Part of an authoritative series Surgical Techniques in Otolaryngology–Head & Neck Surgery Other topics in this comprehensive series include: Head and Neck Surgery, Otologic and Neurotologic Surgery, Laryngeal Surgery, Pediatriac Otolaryngologic Surgery and Facial Plastic and Reconstructive Surgery
This is my artistic approach to creating a Punjabi cultural dialogue with 83 pages of photographed and graphically reproduced mixed media illustrations, artworks and digital works, combined with a hand written and a typed collection of "Gallan" (Conversations). These illustrations and conversations about Punjabi culture, poetry, music, art genres and events are inspired by Punjabi Alphabets called "Gurmukhi". This book is for anyone interested in Punjabi culture, learning basic Punjabi language, vocabulary and grammar. I want this book to offer the audience not only an insight into the culture but ignites curiosity to know and want more. This book seamlessly targets an audience of varied interests and age groups by including: -An art lesson -Complete instructions for an outdoor game of Marbles -Each artwork, illustrations and graphics are signed or initialized. -Fully translated folk song and a poem by legendary poet. -Self portrait of the artist
This new edition provides clinicians with the latest advances in the identification, diagnosis and management of acute and chronic pain conditions and syndromes. Beginning with an overview of pain evaluation, the next chapters explain acute and chronic pain. The following chapters examine different types of pain including cancer, thoracic, lower back, head and neck, and more. Each chapter has been fully revised and the third edition features many new topics, including a complete chapter dedicated to opioid pharmacology. Authored by recognised Texas-based experts in the field, the text is presented in a clear, algorithmic approach, enhanced by clinical photographs and figures. Key points Fully revised, third edition presenting latest advances in diagnosis and management pain Features many new topics including a chapter on opioid pharmacology Authored by recognised Texas-based experts in the field Previous edition (9780323019743) published in 2006
A new edition of a graduate-level machine learning textbook that focuses on the analysis and theory of algorithms. This book is a general introduction to machine learning that can serve as a textbook for graduate students and a reference for researchers. It covers fundamental modern topics in machine learning while providing the theoretical basis and conceptual tools needed for the discussion and justification of algorithms. It also describes several key aspects of the application of these algorithms. The authors aim to present novel theoretical tools and concepts while giving concise proofs even for relatively advanced topics. Foundations of Machine Learning is unique in its focus on the analysis and theory of algorithms. The first four chapters lay the theoretical foundation for what follows; subsequent chapters are mostly self-contained. Topics covered include the Probably Approximately Correct (PAC) learning framework; generalization bounds based on Rademacher complexity and VC-dimension; Support Vector Machines (SVMs); kernel methods; boosting; on-line learning; multi-class classification; ranking; regression; algorithmic stability; dimensionality reduction; learning automata and languages; and reinforcement learning. Each chapter ends with a set of exercises. Appendixes provide additional material including concise probability review. This second edition offers three new chapters, on model selection, maximum entropy models, and conditional entropy models. New material in the appendixes includes a major section on Fenchel duality, expanded coverage of concentration inequalities, and an entirely new entry on information theory. More than half of the exercises are new to this edition.
The new edition of this popular professional book on artificial intelligence (ML) and machine learning (ML) has been revised for classroom or training use. The new edition provides comprehensive coverage of combined AI and ML theory and applications. Rather than looking at the field from only a theoretical or only a practical perspective, this book unifies both perspectives to give holistic understanding. The first part introduces the concepts of AI and ML and their origin and current state. The second and third parts delve into conceptual and theoretic aspects of static and dynamic ML techniques. The fourth part describes the practical applications where presented techniques can be applied. The fifth part introduces the user to some of the implementation strategies for solving real life ML problems. Each chapter is accompanied with a set of exercises that will help the reader / student to apply the learnings from the chapter to a real-life problem. Completion of these exercises will help the reader / student to solidify the concepts learned. The book is appropriate for students in graduate and upper undergraduate courses in addition to researchers and professionals. It makes minimal use of mathematics to make the topics more intuitive and accessible. The book covers a large gamut of topics in the area of AI and ML and a professor can tailor a course on AI / ML based on the book by selecting and re-organizing the sequence of chapters to suit the needs.
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