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Selected papers from the 11th international symposium on electromagnetics fields in electrical engineering ISEF 2003 by Wiak, S.

Table of Contents: “…Editorial advisory board; Abstracts; Editorial; Application of Haar's wavelets in the method of moments to solve electrostatic problems; A 3D multimodal FDTD algorithm for electromagnetic and acoustic propagation in curved waveguides and bent ducts of varying cross-section; The highly efficient three-phase small induction motors with stator cores made from amorphous iron; Optimal shape design of a high-voltage test arrangement; Cogging torque calculation considering magnetic anisotropy for permanent magnet synchronous motors; Magnetoelastic coupling and Rayleigh damping…”
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Automata and Computability. by Kozen, Dexter, 1951-

Table of Contents: “…Miscellaneous Exercises Turing Machines and Effective ComputabilityHints for Selected Miscellaneous Exercises; Solutions to Selected Miscellaneous Exercises; References; Notation and Abbreviations; Index.…”
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Numerical Methods for Eigenvalue Problems. by Börm, Steffen

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Deep Learning with Pytorch Quick Start Guide : Learn to Train and Deploy Neural Network Models in Python. by Julian, David

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Windows 2000 Graphics API Black Book. by Chandler, Damon

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Mastering Scala machine learning by Kozlov, Alexander

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Media Backends : Digital Infrastructures and Sociotechnical Relations. by Parks, Lisa

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New developments in lasers and electro-optics research

Table of Contents: “…Cho -- Shape detection by means of a laser line and approximation neural networks / J. …”
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Digital workflows in architecture : designing design -- designing assembly -- designing industry

Table of Contents: “…DIGITAL CRAFTSMANSHIP: FROM THINKING TO MODELING TO BUILDINGWireframe Algorithms (Editor's Notes); ALGORITHMIC WORKFLOWS IN ASSOCIATIVE MODELING; Workflow Teams (Editor's Notes); WORKFLOW CONSULTANCY; THE SCENT OF THE SYSTEM; indeterminacy (Editor's Notes); DESIGNING INDUSTRY; WHAT DO WE MEAN BY BUILDING DESIGN?…”
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Insight into Theoretical and Applied Informatics : Introduction to Information Technologies and Computer Science. by Yatsko, Andrzej

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Vision Geometry. by Melter, Robert A.

Table of Contents: “…Star-Shapedness of Digitized Planar ShapesAlgorithms for the Decomposition of Convex Polygons -- Decomposition of Discrete Curves into Piecewise Straight Segments in Linear Time -- Digitization Schemes and the Recognition of Digital Straight Lines, Hyperplanes, and Flats in Arbitrary Dimensions -- Computational Geometry and Computer Vision -- Convexity, Visibility, and Orthogonal Polygons…”
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Building Machine Learning Systems with Python. by Richert, Willi

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Java Data Mining - Strategy, Standard, and Practice : a Practical Guide for architecture, design, and implementation. by Hornick, Mark F.

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Principles of Quantum Computation and Information, Volume I : Basic Concepts. by Casati, Giulio, Strini, Giuliano

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Principles of artificial neural networks by Graupe, Daniel

Table of Contents: “…Fundamentals of biological neural networks -- ch. 3. Basic principles of ANNs and their early structures. 3.1. Basic principles of ANN design. 3.2. …”
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Introduction to graph theory by Voloshin, Vitaly I. (Vitaly Ivanovich), 1954-

Table of Contents: “…What Is Mathematical Induction; 7.2. Graph Theory Algorithms and Their Complexity; 7.3. Answers and Hints to Selected Exercises; 7.4. …”
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In silico dreams : how artificial intelligence and biotechnology will create the medicines of the future by Hilbush, Brian

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Python unlocked : become more fluent in Python--learn strategies and techniques for smart and high-performance Python programming by Tigeraniya, Arun

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Deep Learning Quick Reference : Useful hacks for training and optimizing deep neural networks with TensorFlow and Keras. by Bernico, Michael

Table of Contents: “…Drawbacks to consider when using a neural network for regressionUsing deep neural networks for regression; How to plan a machine learning problem; Defining our example problem; Loading the dataset; Defining our cost function; Building an MLP in Keras; Input layer shape; Hidden layer shape; Output layer shape; Neural network architecture; Training the Keras model; Measuring the performance of our model; Building a deep neural network in Keras; Measuring the deep neural network performance; Tuning the model hyperparameters; Saving and loading a trained Keras model; Summary.…”
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Ranking the Liveability of the World's Major Cities : the Global Liveable Cities Index (GLCI). by Tan, Khee Giap

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