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Healthcare Applications of Neuro-Symbolic Artificial Intelligence provides a comprehensive introduction to the field of neuro-symbolic (NS) artificial intelligence (AI), presenting the most recent advances in deep learning and integration of NS systems and large...
This book is designed to provide in-depth knowledge on how search plays a fundamental role in problem solving. Meant for undergraduate and graduate students pursuing courses in computer science and artificial intelligence, it covers a wide spectrum of search methods....
Using diverse real-world examples, this text examines what models used for data analysis mean in a specific research context. What assumptions underlie analyses, and how can you check them? Building on the successful 'Data Analysis and GraphicsUsing R,' 3rd edition...
Learn to build cost-effective apps using Large Language Models In Large Language Model-Based Solutions: How to Deliver Value with Cost-Effective Generative AI Applications, Principal Data Scientist at Amazon Web Services, Shreyas Subramanian, delivers a practical guide...
Privacy-preserving computing aims to protect the personal information of users while capitalizing on the possibilities unlocked by big data. This practical introduction for students, researchers, and industry practitioners is the first cohesive and systematic...
Digital health translation is an important application of machine translation and multilingual technologies, and there is a growing need for accessibility in digital health translation design for disadvantaged communities. This book addresses that need by highlighting...
Distributional semantics develops theories and methods to represent the meaning of natural language expressions, with vectors encoding their statistical distribution in linguistic contexts. It is at once a theoretical model to express meaning, a practical methodology to...
Fully revised and updated, this third edition includes three new chapters on neural networks and deep learning including generative AI, causality, and the social, ethical and regulatory impacts of artificial intelligence. All parts have been updated with the methods...
Space and time representation in language is important in linguistics and cognitive science research, as well as artificial intelligence applications like conversational robots and navigation systems. This book is the first for linguists and computer scientists that...
On social media, new forms of communication arise rapidly, many of which are intense, dispersed, and create new communities at a global scale. Such communities can act as distinct information bubbles with their own perspective on the world, and it is difficult for...
Event structures are central in Linguistics and Artificial Intelligence research: people can easily refer to changes in the world, identify their participants, distinguish relevant information, and have expectations of what can happen next. Part of this process is based...
Language resources and computational models are becoming increasingly important for the study of language variation. A main challenge of this interdisciplinary field is that linguistics researchers may not be familiar with these helpful computational tools and many NLP...
This third edition of Braun and Murdoch's bestselling textbook now includes discussion of the use and design principles of the tidyverse packages in R, including expanded coverage of ggplot2, and R Markdown. The expanded simulation chapter introduces the Box–Muller and...
Recent developments in artificial intelligence, especially neural network and deep learning technology, have led to rapidly improving performance in voice assistants such as Siri and Alexa. Over the next few years, capability will continue to improve and become...
This is a practical guide to P-splines, a simple, flexible and powerful tool for smoothing. P-splines combine regression on B-splines with simple, discrete, roughness penalties. They were introduced by the authors in 1996 and have been used in many diverse applications....
With a machine learning approach and less focus on linguistic details, this gentle introduction to natural language processing develops fundamental mathematical and deep learning models for NLP under a unified framework. NLP problems are systematically organised by...
Sentiment analysis is the computational study of people's opinions, sentiments, emotions, moods, and attitudes. This fascinating problem offers numerous research challenges, but promises insight useful to anyone interested in opinion analysis and social media analysis....
Deep learning is revolutionizing how machine translation systems are built today. This book introduces the challenge of machine translation and evaluation - including historical, linguistic, and applied context -- then develops the core deep learning methods used for...
With the increasing public interest in artificial intelligence (AI), there is also increasing interest in learning about the benefits that AI can deliver to society. This book focuses on research advances in AI that benefit the conservation of wildlife, forests, coral...
Meaningful use of advanced Bayesian methods requires a good understanding of the fundamentals. This engaging book explains the ideas that underpin the construction and analysis of Bayesian models, with particular focus on computational methods and schemes. The unique...
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