Eye Tracking With Python And Pylink
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Eye-Tracking with Python and Pylink
Several Python programming books feature tools designed for experimental psychologists. What sets this book apart is its focus on eye-tracking. Eye-tracking is a widely used research technique in psychology and neuroscience labs. Research grade eye-trackers are typically faster, more accurate, and of course, more expensive than the ones seen in consumer goods or usability labs. Not surprisingly, a successful eye-tracking study usually requires sophisticated computer programming. Easy syntax and flexibility make Python a perfect choice for this task, especially for psychology researchers with little or no computer programming experience. This book offers detailed coverage of the Pylink library, a Python interface for the gold standard EyeLink ® eye-trackers, with many step-by-step example scripts. This book is a useful reference for eye-tracking researchers, but you can also use it as a textbook for graduate-level programming courses.
Human-Friendly Robotics 2024
This book covers a wide range of topics related to human-robot interaction, both physical and cognitive, including theories, methodologies, technologies, and experimental studies. An increasing number of robots operate closely with humans and are expected to interact with people in everyday life, such as patients in hospitals, children in schools, or elderly people in domestic environments. New generations of human-friendly robots must meet the needs of these non-expert users, ensuring safety and trustworthiness both physically and cognitively. To realize this vision, the International Workshop on Human-Friendly Robotics (HFR) provides an opportunity for academic, research, and industry experts to exchange and share their experiences and achievements related to the integration of robots into everyday life. The 17th edition of HFR took place in Lugano, Switzerland, on September 30 and October 1, 2024, and organized by Dalle Molle Institute for Artificial Intelligence (IDSIA), USI-SUPSI.
Introduction to Deep Learning
A project-based guide to the basics of deep learning. This concise, project-driven guide to deep learning takes readers through a series of program-writing tasks that introduce them to the use of deep learning in such areas of artificial intelligence as computer vision, natural-language processing, and reinforcement learning. The author, a longtime artificial intelligence researcher specializing in natural-language processing, covers feed-forward neural nets, convolutional neural nets, word embeddings, recurrent neural nets, sequence-to-sequence learning, deep reinforcement learning, unsupervised models, and other fundamental concepts and techniques. Students and practitioners learn the basics of deep learning by working through programs in Tensorflow, an open-source machine learning framework. “I find I learn computer science material best by sitting down and writing programs,” the author writes, and the book reflects this approach. Each chapter includes a programming project, exercises, and references for further reading. An early chapter is devoted to Tensorflow and its interface with Python, the widely used programming language. Familiarity with linear algebra, multivariate calculus, and probability and statistics is required, as is a rudimentary knowledge of programming in Python. The book can be used in both undergraduate and graduate courses; practitioners will find it an essential reference.