Search Results - (((((((want OR pants) OR wanting) OR markant) OR cantor) OR anne) OR shape) OR hints) algorithms.

Mastering Scala machine learning by Kozlov, Alexander

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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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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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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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Comorbidity in migraine

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Slantwise moves : games, literature, and social invention in nineteenth-century America by Guerra, Douglas

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Insight into Theoretical and Applied Informatics : Introduction to Information Technologies and Computer Science. by Yatsko, Andrzej

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Solutions in lidar profiling of the atmosphere by Kovalev, Vladimir A.

Table of Contents: “…1.6.1 Algorithm and Solution Uncertainty1.6.2 Numerical Simulations and Experimental Data; 1.7 Examination of the Remaining Offset in the Backscatter Signal by~Analyzing the Shape of the Integrated Signal; 1.8 Issues in the Examination of the Lidar Overlap Function; 1.8.1 Influence of Distortions in the Lidar Signal when Determining the~Overlap Function; 1.8.2 Issues of Lidar Signal Inversion within the Incomplete Overlap Area; Chapter 2 Essentials and Issues in Separating the Backscatter and Transmission Terms in The Lidar Equation.…”
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Computational intelligence in aerospace sciences

Table of Contents: “…Guenov, Mrco Nunez, Arturo Molina-Cristóbal, Vis Sripawadkul, varun Datta and Atif Riaz ; Surrogate modeling in the service of multidisciplinary design / Stephen Powell and András Sóbester ; Multidisciplinary design optimization of aerospace transportation systems / Edmondo Minisci and Massimilliano Vasile -- Aerodynamics optimization: Aerodynamic shape design using evolutionary computation : a tutorial with examples and case studies / Domenico Quagliarella ; Multiobjective design optimization using Nash games / Jean-Antoine Désidéri and Régis Duvigneau ; Design rule extraction using multiobjective design exploration / Shigeru Obayashi -- Space trajectory design: Automated interplanetary mission planning / Jacob Englander and Bruce Conway ; On the global optimization of multigravity assist trajectories with evolutionary algorithms / Massimilliano Vasile and Edmondo Minisci, Marco Locatelli ; An incremental algorithm for the optimization of multiple gravity assist trajectories / Massimilliano Vasile, Matteo Ceriotti, Victor M. …”
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Parts-feeding systems for assembly : organisation, logistics and automation

Table of Contents: “…A model for kitting operations planningRobust optimization approach to production system with failure in rework and breakdown under uncertainty: evolutionary methods; Re-layout of an assembly area: a case study at Bosch Rexroth Oil Control; A simple mechanical measurement system for the posture evaluation of wing components using the PSO and ICP algorithms; Implementation framework for a fully flexible assembly system (F-FAS); A genetic algorithm for supermarket location problem; New Kanban model for tow-train feeding system design.…”
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Principles of Quantum Computation and Information, Volume I : Basic Concepts. by Casati, Giulio, Strini, Giuliano

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Building Machine Learning Systems with Python. by Richert, Willi

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3D face modeling, analysis and recognition by Daoudi, Mohamed, Ph. D., Srivastava, Anuj, Veltkamp, Remco C., 1963-

Table of Contents: “…3.5.4 Extension to Facial Surfaces Shape Analysis3.6 The Dense Scalar Field (DSF); 3.7 Statistical Shape Analysis; 3.7.1 Statistics on Manifolds: Karcher Mean; 3.7.2 Learning Statistical Models in Shape Space; 3.8 Applications of Statistical Shape Analysis; 3.8.1 3D Face Restoration; 3.8.2 Hierarchical Organization of Facial Shapes; 3.9 The Iso-geodesic Stripes; 3.9.1 Extraction of Facial Stripes; 3.9.2 Computing Relationships between Facial Stripes; 3.9.3 Face Representation and Matching Using Iso-geodesic Stripes; Exercises; Glossary; References.…”
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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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Wavelet theory approach to pattern recognition

Table of Contents: “…Comparison with other wavelets. 7.4. Algorithm and experiments -- ch. 8. Skeletonization of ribbon-like shapes with new wavelet function. 8.1. …”
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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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Data clustering in C++ : an object-oriented approach by Gan, Guojun, 1979-

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The Finite Element Method : a Practical Course. by Liu, G. R.

Table of Contents: “…3.4.3.3 On other means of construct shape functions3.4.4 Properties of the shape functions; 3.4.5 Formulation of finite element equations in local coordinate system; 3.4.6 Coordinate transformation; 3.4.7 Assembly of global FE equation; 3.4.8 Imposition of displacement constraints; 3.4.9 Solving the global FE equation; 3.5 Static analysis; 3.6 Analysis of free vibration (eigenvalue analysis); 3.7 Transient response; 3.7.1 Central difference algorithm; 3.7.2 Newmark's method (Newmark, 1959); 3.8 Remarks; 3.8.1 Summary of shape function properties.…”
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