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This book is an introduction to machine learning, with a strong focus on the mathematics behind the standard algorithms and techniques in the field, aimed at senior undergraduates and early graduate students of Mathematics. There is a focus on well-known supervised machine learning algorithms, detailing the existing theory to provide some theoretical guarantees, featuring intuitive proofs and exposition of the material in a concise and precise manner. A broad set of topics is covered, giving an overview of the field. A summary of the topics covered is: statistical learning theory, approximation theory, linear models, kernel methods, Gaussian processes, deep neural networks, ensemble methods and unsupervised learning techniques, such as clustering and dimensionality reduction. This book is suited for students who are interested in entering the field, by preparing them to master the standard tools in Machine Learning. The reader will be equipped to understand the main theoretical questions of the current research and to engage with the field.
This book explains how to use the programming language Python to develop machine learning and deep learning tasks.
The book focuses on stability and approximation results concerning recent numerical methods for the numerical solution of hyperbolic conservation laws. The work begins with a detailed and thorough introduction of hyperbolic conservation/balance laws and their numerical treatment. In the main part, recent results in such context are presented focusing on the investigation of approximation properties of discontinuous Galerkin and flux reconstruction methods, the construction of (entropy) stable numerical methods and the extension of existing (entropy) stability results for both semidiscrete and fully discrete schemes, and development of new high-order methods.
Exploring the Implications of Complexity Thinking for Translation Studies considers the new link between translation studies and complexity thinking. Edited by leading scholars in this emerging field, the collection builds on and expands work done in complexity thinking in translation studies over the past decade. In this volume, the contributors address a variety of implications that this new approach holds for key concepts in Translation Studies such as source vs. target texts, translational units, authorship, translatorship, for research topics including translation data, machine translation, communities of practice, and for research methods such as constraints and the emergence of trajectories. The various chapters provide valuable information as to how research methods informed by complexity thinking can be applied in translation studies. Presenting theoretical and methodological contributions as well as case studies, this volume is of interest to advanced students, academics, and researchers in translation and interpreting studies, literary studies, and related areas.
Typical timelines to go from discovery to impact in the advanced materials sector are between 10 to 30 years. Advances in robotics and artificial intelligence are poised to accelerate the discovery and development of new materials dramatically. This book is a primer for any materials scientist looking to future-proof their careers and get ahead of the disruption that artificial intelligence and robotic automation is just starting to unleash. It is meant to be an overview of how we can use these disruptive technologies to augment and supercharge our abilities to discover new materials that will solve world’s biggest challenges. Written by world leading experts on accelerated materials discovery from academia (UC Berkeley, Caltech, UBC, Cornell, etc.), industry (Toyota Research Institute, Citrine Informatics) and national labs (National Research Council of Canada, Lawrence Berkeley National Labs).
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Probability is the bedrock of machine learning. You cannot develop a deep understanding and application of machine learning without it. Cut through the equations, Greek letters, and confusion, and discover the topics in probability that you need to know. Using clear explanations, standard Python libraries, and step-by-step tutorial lessons, you will discover the importance of probability to machine learning, Bayesian probability, entropy, density estimation, maximum likelihood, and much more.
This engaging and clearly written textbook/reference provides a must-have introduction to the rapidly emerging interdisciplinary field of data science. It focuses on the principles fundamental to becoming a good data scientist and the key skills needed to build systems for collecting, analyzing, and interpreting data. The Data Science Design Manual is a source of practical insights that highlights what really matters in analyzing data, and provides an intuitive understanding of how these core concepts can be used. The book does not emphasize any particular programming language or suite of data-analysis tools, focusing instead on high-level discussion of important design principles. This easy...
Biogas Production covers the most cutting-edge pretreatment processes being used and studied today for the production of biogas. As an increasingly important piece of the "energy pie," biogas and other biofuels are being used more and more around the world in every conceivable area of industry and could be a partial answer to the energy problem and the elimination of global warming. This book will highlight the recent advances in the pretreatment and value addition of lignocellulosic wastes (LCW) with the main focus on domestic and agro-industrial residues. Mechanical, physical, and biological treatment systems are brought into perspective. The main value-added products from lignocellulosic ...
The specialty of fertility preservation offers patients with cancer, who are rendered infertile by chemo- and radiotherapy, the opportunity to realize their reproductive potential. This gold-standard publication defines the specialty. The full range of techniques and scientific concepts is covered in detail, and the author team includes many of the world's leading experts in the field. The book opens with introductions to fertility preservation in both cancer and non-cancer patients, followed by cancer biology, epidemiology and treatment, and reproductive biology and cryobiology. Subsequent sections cover fertility preservation strategies in males and females, including medical/surgical procedures, ART, cryopreservation and transplantation of both ovarian tissue and the whole ovary, and in-vitro follicle growth and maturation. Concluding chapters address future technologies, as well as ethical, legal and religious issues. Richly illustrated throughout, this is a key resource for all clinicians specializing in reproductive medicine, gynecology, oncology, hematology, endocrinology and infertility.