The Statistical Physics of Data Assimilation and Machine Learning - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook The Statistical Physics of Data Assimilation and Machine Learning write by Henry D. I. Abarbanel. This book was released on 2022-02-17. The Statistical Physics of Data Assimilation and Machine Learning available in PDF, EPUB and Kindle. Data assimilation is a hugely important mathematical technique, relevant in fields as diverse as geophysics, data science, and neuroscience. This modern book provides an authoritative treatment of the field as it relates to several scientific disciplines, with a particular emphasis on recent developments from machine learning and its role in the optimisation of data assimilation. Underlying theory from statistical physics, such as path integrals and Monte Carlo methods, are developed in the text as a basis for data assimilation, and the author then explores examples from current multidisciplinary research such as the modelling of shallow water systems, ocean dynamics, and neuronal dynamics in the avian brain. The theory of data assimilation and machine learning is introduced in an accessible and unified manner, and the book is suitable for undergraduate and graduate students from science and engineering without specialized experience of statistical physics.
Machine Learning with Neural Networks
Machine Learning with Neural Networks - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Machine Learning with Neural Networks write by Bernhard Mehlig. This book was released on 2021-10-28. Machine Learning with Neural Networks available in PDF, EPUB and Kindle. This modern and self-contained book offers a clear and accessible introduction to the important topic of machine learning with neural networks. In addition to describing the mathematical principles of the topic, and its historical evolution, strong connections are drawn with underlying methods from statistical physics and current applications within science and engineering. Closely based around a well-established undergraduate course, this pedagogical text provides a solid understanding of the key aspects of modern machine learning with artificial neural networks, for students in physics, mathematics, and engineering. Numerous exercises expand and reinforce key concepts within the book and allow students to hone their programming skills. Frequent references to current research develop a detailed perspective on the state-of-the-art in machine learning research.
Deep Learning and Physics
Deep Learning and Physics - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Deep Learning and Physics write by Akinori Tanaka. This book was released on 2021-03-24. Deep Learning and Physics available in PDF, EPUB and Kindle. What is deep learning for those who study physics? Is it completely different from physics? Or is it similar? In recent years, machine learning, including deep learning, has begun to be used in various physics studies. Why is that? Is knowing physics useful in machine learning? Conversely, is knowing machine learning useful in physics? This book is devoted to answers of these questions. Starting with basic ideas of physics, neural networks are derived naturally. And you can learn the concepts of deep learning through the words of physics. In fact, the foundation of machine learning can be attributed to physical concepts. Hamiltonians that determine physical systems characterize various machine learning structures. Statistical physics given by Hamiltonians defines machine learning by neural networks. Furthermore, solving inverse problems in physics through machine learning and generalization essentially provides progress and even revolutions in physics. For these reasons, in recent years interdisciplinary research in machine learning and physics has been expanding dramatically. This book is written for anyone who wants to learn, understand, and apply the relationship between deep learning/machine learning and physics. All that is needed to read this book are the basic concepts in physics: energy and Hamiltonians. The concepts of statistical mechanics and the bracket notation of quantum mechanics, which are explained in columns, are used to explain deep learning frameworks. We encourage you to explore this new active field of machine learning and physics, with this book as a map of the continent to be explored.
The Principles of Deep Learning Theory
The Principles of Deep Learning Theory - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook The Principles of Deep Learning Theory write by Daniel A. Roberts. This book was released on 2022-05-26. The Principles of Deep Learning Theory available in PDF, EPUB and Kindle. This volume develops an effective theory approach to understanding deep neural networks of practical relevance.
Data Assimilation and Control: Theory and Applications in Life Sciences
Data Assimilation and Control: Theory and Applications in Life Sciences - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Data Assimilation and Control: Theory and Applications in Life Sciences write by Axel Hutt. This book was released on 2019-08-16. Data Assimilation and Control: Theory and Applications in Life Sciences available in PDF, EPUB and Kindle. The understanding of complex systems is a key element to predict and control the system’s dynamics. To gain deeper insights into the underlying actions of complex systems today, more and more data of diverse types are analyzed that mirror the systems dynamics, whereas system models are still hard to derive. Data assimilation merges both data and model to an optimal description of complex systems’ dynamics. The present eBook brings together both recent theoretical work in data assimilation and control and demonstrates applications in diverse research fields.
