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Learning from data streams : Processing techniques in sensor networks
The book provides the reader with a comprehensive overview of stream data processing, including famous prototype implementations like the Nile system and the TinyOS operating system. The set of chapters covers the state-of-art in data stream mining approaches using clustering, predictive learning, and tensor analysis techniques, and applying them to applications in security, the natural sciences, and education.
Beginning deep learning with TensorFlow : Work with Keras, MNIST data sets, and advanced neural networks
Stats with an introduction to AI, where you’ll learn the history of neural networks and what sets deep learning apart from other varieties of machine learning. Discovery the variety of deep learning frameworks and set-up a deep learning development environment. Next, you’ll jump into simple classification programs for hand-writing analysis. Once you’ve tackled the basics of deep learning, you move on to TensorFlow 2 specifically. Find out what exactly a Tensor is and how to work with MNIST datasets. Finally, you’ll get into the heavy lifting of programming neural networks and working with a wide variety of neural network types such as GANs and RNNs. Deep Learning is a new area of Machine Learning research widely used in popular applications, such as voice assistant and self-driving cars. Work through the hands-on material in this book and become a TensorFlow programmer! You will: Develop using deep learning algorithms Build deep learning models using TensorFlow 2 Create classification systems and other, practical deep learning applications
Applied Deep Learning with TensorFlow 2 : Learn to Implement Advanced Deep Learning Techniques with Python
Focuses on the fundamental concepts and at the same time on practical aspects of implementing neural networks and deep learning for your research projects. This book is designed so that you can focus on the parts you are interested in. You will explore topics as regularization, optimizers, optimization, metric analysis, and hyper-parameter tuning. In addition, you will learn the fundamentals ideas behind autoencoders and generative adversarial networks. All the code presented in the book will be available in the form of Jupyter notebooks which would allow you to try out all examples and extend them in interesting ways. A companion online book is available with the complete code for all examples discussed in the book and additional material more related to TensorFlow and Keras. All the code will be available in Jupyter notebook format and can be opened directly in Google Colab (no need to install anything locally) or downloaded on your own machine and tested locally. You will: Understand the fundamental concepts of how neural networks work / Learn the fundamental ideas behind autoencoders and generative adversarial networks / Be able to try all the examples with complete code examples that you can expand for your own projects / Have available a complete online companion book with examples and tutorials.
Anisotropy Across Fields and Scales
This book focuses on processing, modeling, and visualization of anisotropy information…
Advanced mathematical science for mobility society
The automotive industry has made steady progress in technological innovations under the names of Connected Autonomous-Shared-Electric (CASE) and Mobility as a Service (MaaS). Needless to say, mathematics and informatics are important to support such innovations. As the concept of cars and movement itself is diversifying, they are indispensable for grasping the essence of the future mobility society and building the foundation for the next generation. This book contains three main contents. 1. Mathematical models of flow 2. Mathematical methodsfor huge data and network analysis 3. Algorithm for mobility society The first one discusses mathematical models of pedestrian and traffic flow, as they are important for preventing accidents and achieving efficient transportation.
Basal Implantology
Helps oral implantologists to understand the principles that underlie the use of basal implants as a means to provide simple solutions to complex and highly demanding clinical situations without the need for prior bone grafting.
Machine learning for risk calculations : A practitioner's view
Fundamental Approximation Methods. Machine Learning -- Deep Neural Nets -- Chebyshev Tensors -- The toolkit - plugging in approximation methods. Introduction: why is a toolkit needed -- Composition techniques -- Tensors in TT format and Tensor Extension Algorithms -- Sliding Technique -- The Jacobian projection technique -- Hybrid solutions - approximation methods and the toolkit.
Magnetic Functions Beyond the Spin-Hamiltonian
Using the spin-Hamiltonian formalism the magnetic parameters are introduced through the components of the Lambda-tensor involving only the matrix elements of the angular momentum operator. The energy levels for a variety of spins are generated and the modeling of the magnetization, the magnetic susceptibility and the heat capacity is done. Theoretical formulae necessary in performing the energy level calculations for a multi-term system are prepared with the help of the irreducible tensor operator approach. The goal of the programming lies in the fact that the entire relevant matrix elements (electron repulsion, crystal field, spin-orbit interaction, orbital-Zeeman, and spin-Zeeman operators) are evaluated in the basis set of free-atom terms. The modeling of the zero-field splitting is done at three levels of sophistication. The spin-Hamiltonian formalism offers simple formulae for the magnetic parameters by evaluating the matrix elements of the angular momentum operator in the basis set of the crystal-field terms. The magnetic functions for dn complexes are modeled for a wide range of the crystal-field strengths.
Linear Algebra Thoroughly Explained
Linear Algebra Thoroughly Explained provides a comprehensive introduction to the subject suitable for adoption as a self-contained text for courses at undergraduate and postgraduate level. The clear and comprehensive presentation of the basic theory is illustrated throughout with an abundance of worked examples. The book is written for teachers and students of linear algebra at all levels and across mathematics and the applied sciences, particularly physics and engineering.
Lie Groups : An Approach through Invariants and Representations
Lie groups has been an increasing area of focus and rich research since the middle of the 20th century. Procesi's masterful approach to Lie groups through invariants and representations gives the reader a comprehensive treatment of the classical groups along with an extensive introduction to a wide range of topics associated with Lie groups: symmetric functions, theory of algebraic forms, Lie algebras, tensor algebra and symmetry, semisimple Lie algebras, algebraic groups, group representations, invariants, Hilbert theory, and binary forms with fields ranging from pure algebra to functional analysis.
Complex Nonlinearity : Chaos, Phase Transitions, Topology Change and Path Integrals
The book starts with a textbook-like expose on nonlinear dynamics, attractors and chaos, both temporal and spatio-temporal, including modern techniques of chaos–control. Chapter 2 turns to the edge of chaos, in the form of phase transitions (equilibrium and non-equilibrium, oscillatory, fractal and noise-induced), as well as the related field of synergetics. While the natural stage for linear dynamics comprises of flat, Euclidean geometry (with the corresponding calculation tools from linear algebra and analysis), the natural stage for nonlinear dynamics is curved, Riemannian geometry (with the corresponding tools from nonlinear, tensor algebra and analysis). The extreme nonlinearity – chaos – corresponds to the topology change of this curved geometrical stage, usually called configuration manifold. Chapter 3 elaborates on geometry and topology change in relation with complex nonlinearity and chaos. Chapter 4 develops general nonlinear dynamics, continuous and discrete, deterministic and stochastic, in the unique form of path integrals and their action-amplitude formalism.
Classical Electromagnetic Theory
This book stresses the unity of electromagnetic theory with electric and magnetic fields developed in parallel. SI units are used throughout and considerable use is made of tensor notation and the Levi-Cevita symbol. To more closely display the parallelism, extensive use is made of the scalar magnetic potential particularly in dealing with the Laplace and Poisson equation. 85 worked problems illustrate the theory. Conformal mappings are dealt with in some detail. Relevant mathematical material is provided in appendices.
Categories and Sheaves
This book covers categories, homological algebra and sheaves in a systematic and exhaustive manner starting from scratch, and continues with full proofs to an exposition of the most recent results in the literature, and sometimes beyond.The authors present the general theory of categories and functors, emphasising inductive and projective limits, tensor categories, representable functors, ind-objects and localization. Then they study homological algebra including additive, abelian, triangulated categories and also unbounded derived categories using transfinite induction and accessible objects. Finally, sheaf theory as well as twisted sheaves and stacks appear in the framework of Grothendieck topologies.
Atomistic modeling of materials failure
Atomistic Modeling of Materials Failure is an introduction to molecular and atomistic modeling techniques applied to solid deformation and fracture. Focusing on a variety of brittle, ductile and geometrically confined materials, this detailed overview includes computational methods at the atomic scale, and describes how these techniques can be used to model the dynamics of cracks, dislocations and other deformation mechanisms.
An introduction to differential geometry with applications to elasticity
Interestingly, notions that pertain to di?erential geometry per se,suchas covariant derivatives of tensor ?elds, are also introduced in Chapters 3 and 4, where they appear most naturally in the derivation of the basic boundary value problems of three-dimensional elasticity and shell theory.
Advanced Linear Algebra
For the third edition, the author has added a new chapter on associative algebras that includes the well known characterizations of the finite-dimensional division algebras over the real field (a theorem of Frobenius) and over a finite field (Wedderburn's theorem); polished and refined some arguments (such as the discussion of reflexivity, the rational canonical form, best approximations and the definitions of tensor products); upgraded some proofs that were originally done only for finite-dimensional/rank cases; added new theorems, including the spectral mapping theorem; considerably expanded the reference section with over a hundred references to books on linear algebra.
Advanced Linear Algebra
The first part of the book contains a careful but rapid discussion of the basics of linear algebra, including vector spaces, linear transformations, quotient spaces, and isomorphism theorems. The author then proceeds to modules, emphasizing a comparison with vector spaces. A thorough discussion of inner product spaces, eigenvalues, eigenvectors, and finite dimensional spectral theory follows, culminating in the finite dimensional spectral theorem for normal operators. The second part of the book is a collection of topics, including metric vector spaces, metric spaces, Hilbert spaces, tensor products, and affine geometry. The last chapter discusses the umbral calculus, an area of modern algebra with many important applications.
Acoustic Emission Testing : Basics for Research - Applications in Civil Engineering
The book covers all levels from the description of AE basics for AE beginners (level of a student) to sophisticated AE algorithms and applications to real large-scale structures as well as the observation of the cracking process in laboratory specimen to study fracture processes.
