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Neuroanatomy
'Neuroanatomy' teaches neuroanatomy in a purely kinesthetic way. In using this work, the reader draws each neuroanatomical pathway and structure, and in the process, creates memorable and reproducible schematics for the various learning points in Neuroanatomy in a hands-on, enjoyable and highly effective manner. In addition to this unique method, it also provides a remarkable repository of reference materials, including numerous anatomic and radiographic brain images and illustrations from many other classic texts to enhance the learning experience
Mastering NLP from Foundations to LLMs
Enhance your NLP proficiency with modern frameworks like LangChain, explore mathematical foundations and code samples, and gain expert insights into current and future trends Key Features Learn how to build Python-driven solutions with a focus on NLP, LLMs, RAGs, and GPT Master embedding techniques and machine learning principles for real-world applications Understand the mathematical foundations of NLP and deep learning designs Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionDo you want to master Natural Language Processing (NLP) but don’t know where to begin? This book will give you the right head start. Written by leaders in machine learning and NLP, Maste...
Large Language Models
Large Language Models (LLMs) have emerged as a cornerstone technology, transforming how we interact with information and redefining the boundaries of artificial intelligence. LLMs offer an unprecedented ability to understand, generate, and interact with human language in an intuitive and insightful manner, leading to transformative applications across domains like content creation, chatbots, search engines, and research tools. While fascinating, the complex workings of LLMs -- their intricate architecture, underlying algorithms, and ethical considerations -- require thorough exploration, creating a need for a comprehensive book on this subject. This book provides an authoritative exploration...
From Deep Learning to Rational Machines
"This book provides a framework for thinking about foundational philosophical questions surrounding machine learning as an approach to artificial intelligence. Specifically, it links recent breakthroughs in deep learning to classical empiricist philosophy of mind. In recent assessments of deep learning's current capabilities and future potential, prominent scientists have cited historical figures from the perennial philosophical debate between nativism and empiricism, which primarily concerns the origins of abstract knowledge. These empiricists were generally faculty psychologists; that is, they argued that the active engagement of general psychological faculties-such as perception, memory, ...
Cognitive Plausibility in Natural Language Processing
This book explores the cognitive plausibility of computational language models and why it’s an important factor in their development and evaluation. The authors present the idea that more can be learned about cognitive plausibility of computational language models by linking signals of cognitive processing load in humans to interpretability methods that allow for exploration of the hidden mechanisms of neural models. The book identifies limitations when applying the existing methodology for representational analyses to contextualized settings and critiques the current emphasis on form over more grounded approaches to modeling language. The authors discuss how novel techniques for transfer and curriculum learning could lead to cognitively more plausible generalization capabilities in models. The book also highlights the importance of instance-level evaluation and includes thorough discussion of the ethical considerations that may arise throughout the various stages of cognitive plausibility research.
Diagnostic Pathology: Endocrine E-Book
This expert volume in the Diagnostic Pathology series is an excellent point-of-care resource for practitioners at all levels of experience and training. Covering all areas of endocrine pathology, it incorporates the most recent clinical, pathologic, and molecular knowledge in this challenging field to provide a comprehensive overview of all key issues relevant to today's practice. Richly illustrated and easy to use, the third edition of Diagnostic Pathology: Endocrine is a one-stop reference for accurate, complete pathology reports, ideal as a day-to-day reference or as a reliable training resource. - Provides essential knowledge in all areas of endocrine pathology, including thyroid, parath...
Molecular Oncology Diagnostics, An Issue of the Clinics in Laboratory Medicine, E-Book
In this issue of Clinics in Laboratory Medicine, guest editor Dr. Fei Dong brings his considerable expertise to the topic of Molecular Oncology Diagnostics. Top experts in the field review current practices in molecular oncology diagnostics, highlighting the rapid clinical adoption of next generation sequencing techniques. The articles address key laboratory considerations in the molecular testing of cancer specimens and the impact of advanced testing strategies on patient care. - Contains 15 practice-oriented topics including utility of single gene testing in cancer specimens; analytical principles of cancer next generation sequencing; principles and validation of bioinformatics pipelines for cancer next generation sequencing; panel sequencing for targeted therapy selection in solid tumors; and more. - Provides in-depth clinical reviews on molecular oncology diagnostics, offering actionable insights for clinical practice. - Presents the latest information on this timely, focused topic under the leadership of experienced editors in the field. Authors synthesize and distill the latest research and practice guidelines to create clinically significant, topic-based reviews.
Computer Vision
Computer vision has made enormous progress in recent years, and its applications are multifaceted and growing quickly, while many challenges still remain. This book brings together a range of leading researchers to examine a wide variety of research directions, challenges, and prospects for computer vision and its applications. This book highlights various core challenges as well as solutions by leading researchers in the field. It covers such important topics as data-driven AI, biometrics, digital forensics, healthcare, robotics, entertainment and XR, autonomous driving, sports analytics, and neuromorphic computing, covering both academic and industry R&D perspectives. Providing a mix of breadth and depth, this book will have an impact across the fields of computer vision, imaging, and AI. Computer Vision: Challenges, Trends, and Opportunities covers timely and important aspects of computer vision and its applications, highlighting the challenges ahead and providing a range of perspectives from top researchers around the world. A substantial compilation of ideas and state-of-the-art solutions, it will be of great benefit to students, researchers, and industry practitioners.
Large Language Models in Cybersecurity
This open access book provides cybersecurity practitioners with the knowledge needed to understand the risks of the increased availability of powerful large language models (LLMs) and how they can be mitigated. It attempts to outrun the malicious attackers by anticipating what they could do. It also alerts LLM developers to understand their work's risks for cybersecurity and provides them with tools to mitigate those risks. The book starts in Part I with a general introduction to LLMs and their main application areas. Part II collects a description of the most salient threats LLMs represent in cybersecurity, be they as tools for cybercriminals or as novel attack surfaces if integrated into e...
