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The Principles of Deep Learning Theory
This volume develops an effective theory approach to understanding deep neural networks of practical relevance.
Computational Mathematics, Algorithms, and Data Processing
“Computational Mathematics, Algorithms, and Data Processing” of MDPI consists of articles on new mathematical tools and numerical methods for computational problems. Topics covered include: numerical stability, interpolation, approximation, complexity, numerical linear algebra, differential equations (ordinary, partial), optimization, integral equations, systems of nonlinear equations, compression or distillation, and active learning.
The Climate Demon
An introduction to the complex world of climate models that explains why we should trust their predictions despite the uncertainties.
Hands-on TinyML
Learn how to deploy complex machine learning models on single board computers, mobile phones, and microcontrollers KEY FEATURES ● Gain a comprehensive understanding of TinyML's core concepts. ● Learn how to design your own TinyML applications from the ground up. ● Explore cutting-edge models, hardware, and software platforms for developing TinyML. DESCRIPTION TinyML is an innovative technology that empowers small and resource-constrained edge devices with the capabilities of machine learning. If you're interested in deploying machine learning models directly on microcontrollers, single board computers, or mobile phones without relying on continuous cloud connectivity, this book is an i...
Geometric and Computational Spectral Theory
A co-publication of the AMS and Centre de Recherches Mathématiques The book is a collection of lecture notes and survey papers based on the mini-courses given by leading experts at the 2015 Séminaire de Mathématiques Supérieures on Geometric and Computational Spectral Theory, held from June 15–26, 2015, at the Centre de Recherches Mathématiques, Université de Montréal, Montréal, Quebec, Canada. The volume covers a broad variety of topics in spectral theory, highlighting its connections to differential geometry, mathematical physics and numerical analysis, bringing together the theoretical and computational approaches to spectral theory, and emphasizing the interplay between the two.
AI in Clinical Practice
AI in Clinical Practice: A Guide to Artificial Intelligence and Digital Medicine explains how artificial intelligence is applied to medicine, illustrating not only its enormous potential but also ancillary issues and the limits and risks inherent in its use on a large scale. The book focuses on the intersection between medicine and AI and its implications on the impact of human health care delivery. Topics discussed include wearable devices, health data, Internet of Things, virtual reality, robotic assistance system, and digital intelligence in the health sector. Additionally, sections discuss diagnostics and decision-making systems and machine/deep learning in clinical setting. This is a va...
Topology and Quantum Theory in Interaction
This volume contains the proceedings of the NSF-CBMS Regional Conference on Topological and Geometric Methods in QFT, held from July 31–August 4, 2017, at Montana State University in Bozeman, Montana. In recent decades, there has been a movement to axiomatize quantum field theory into a mathematical structure. In a different direction, one can ask to test these axiom systems against physics. Can they be used to rederive known facts about quantum theories or, better yet, be the framework in which to solve open problems? Recently, Freed and Hopkins have provided a solution to a classification problem in condensed matter theory, which is ultimately based on the field theory axioms of Graeme S...
Mapping Texts
Mapping Texts is the first introduction to computational text analysis that simultaneously blends conceptual treatments with practical, hands-on examples that walk the reader through how to conduct text analysis projects with real data. The book shows how to conduct text analysis in the R statistical computing environment--a popular programming language in data science.
Putting Ourselves Back in the Equation
Distant galaxies, dark matter, black holes – elusive, incomprehensible and inhospitable – these are the building blocks of modern physics. But where do we fit in this picture? For centuries, we have separated mind from matter. While physicists have pursued a theory of ‘everything’ with single-minded purpose, the matter of the mind, of human consciousness, has been conveniently sidestepped and ignored – consigned to priests, philosophers and poets. With the ambition of Stephen Hawking, Carlo Rovelli and Brian Cox, Putting Ourselves Back in the Equation sets out a bold new vision for theoretical physics, unrestricted by sleek equations and neat formulations. Combining cutting-edge neuroscience with the latest in quantum mechanics, acclaimed writer Musser offers a new interpretation of human consciousness. From bizarre cognitive phenomena, like lucid dreaming and self-taught synaesthesia, to the latest technological developments in AI, Musser asks: what can physics teach us about what it means to be human?
