Search Results - Max Born~

The Protestant ethic and the spirit of capitalism / by Weber, Max 1864-1920

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Heirloom wood : a modern guide to carving spoons, bowls, boards, and other homewares / by Bainbridge, Max

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Xgboost algoritması ile pay piyasası fiyat tahmini: borsa İstanbul endeksleri üzerinde bir uygulama / by Tayır, Tahsin

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Entrepreneurship

Table of Contents: “…Aldrich -- Economic freedom or self-imposed strife: work-life conflict, gender, and self-employment / Jeremy Reynolds and Linda A. …”
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Governance and performance in public and non-profit organizations

Table of Contents: “…Rea, Giulia Bellante -- From governance to action: measuring the engagement of active stakeholders in the social enterprise / Rita Bissola, Barbara Imperatori -- Interrogating the unilateral influence of transformational leadership: top-down to bottom-up individualized consideration in the board-executive director relationship / Gregory Bott -- Fostering orientation to performance in nonprofit organizations through control and coordination: the case of corporate foundations and founder firms / Marco Minciullo -- Literature review: measuring and assessing organizational performance for non-profits, contextually sensitive standards and measures for the non-profit organization / Lisa Morrison.…”
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Paradigms of combinatorial optimization /

Table of Contents: “…ILP models for level packing; 5.3. Upper bounds; 5.3.1. Strip packing; 5.3.2. Bin packing: two-phase heuristics; 5.3.3. …”
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Advances in Robot Kinematics 2018

Table of Contents: “…-Anirban Nag, Sandipan Bandyopadhyay -- Higher-order relative kinematics of rigid body motions. A dual Lie algebra approach, by Daniel Condurache -- An Algorithm for Trajectory Generation in Redundant Manipulators with Joint Transmission Accommodation, by Bahram Ravani, Kristopher Wehage -- Evaluation of Dynamic Relaxation to Solve Kinematics of Concentric Tube Robots, by Quentin Peyron, Kanty Rabenorosoa, Nicolas Andreff, Pierre Renaud -- Iterative Method for the Inverse Kinematics of a 3-Limb Parallel Mechanism with 3-DOF Using a 6-Limb Mechanism with 6-DOF, by Xinghai Liang, Yukio Takeda -- Optimal Object Placement using a Virtual Axis, by Martin Georg Weiß -- The Forward Kinematics of Doubly-Planar Gough-Stewart Platforms and the Position Analysis of Strips of Tetrahedra, by Josep M Porta, Federico Thomas -- Six-bar Linkage Design System with a Parallelized Polynomial Homotopy Solver, by Jeffrey Glabe, John Michael McCarthy -- Algebraic Analysis of a 3-RUU Parallel Manipulator, by Thomas Stigger, Abhilash Nayak, Philippe Wenger, Stéphane Caro, Martin Pfurner, Manfred Husty -- Singularities -- Kinematic analysis of planar tensegrity 2-X manipulators, by Matthieu FURET, Philippe Wenger, Max Lettl -- Rotational Mobility Analysis of the 3-RFR Class of Spherical Parallel Robots, by David Corinaldi, Luca Carbonari, Matteo Palpacelli, Massimo Callegari -- Randomized Planning of Dynamic Motions Avoiding Forward Singularities, by Ricard Bordalba, Lluís Ros, Josep M Porta -- Analysis of Kinematic Singularities for a Serial Redundant Manipulator with 7 DOF, by Zijia Li, Mathias Brandstötter, Michael Hofbaur -- A Geometric Method of Singularity Avoidance for Kinematically Redundant Planar Parallel Robots, by Nicholas Baron, Andrew Philippides, Nicolas Rojas -- The 3-PPPS parallel robot with U-shape Base, a 6-DOF parallel robot with simple kinematics, by Damien Chablat, Luc Baron, Ranjan Jha, Luc Rolland -- On the singularities of a parallel robotic system used in elbow and wrist rehabilitation, by Iosif Birlescu, Bogdan Gherman, Calin Vaida, Doina Pisla, Nicolae PLITEA, Adrian PISLA, Giuseppe Carbone -- Kinematic constraint maps and C-space singularities for planar mechanisms with prismatic joints, by Seyedvahid Amirinezhad, Peter Donelan -- Transversality and its applications to kinematics, by Seyedvahid Amirinezhad, Peter Donelan, Andreas Mueller -- Control and Dynamics -- Lateral Stability of a 3-DOF Asymmetrical Spherical Parallel Manipulator with a Universal Joint Featuring Infinite Torsional Movement, by Guanglei Wu, Huiping Shen -- On the Use of Instant Centers to Build Dynamic Models of Single-dof Planar Mechanisms, by Raffaele Di-Gregorio, Guanglei Wu, Huiping Shen -- Normal forms of robotic systems with affine Pfaffian constraints: A case study, by Krzysztof Tchon, Joanna Ratajczak, Janusz Jakubiak -- Experimental Identification of Stress-Strain Material Models of UHMWPE Fiber Cables for Improving Cable Tension Control Strategies, by Philipp Tempel, Felix Trautwein, Andreas Pott -- Modelling -- A General Discretization-based Approach for the Kinetostatic Analysis of Closed-loop Rigid/Flexible Hybrid Mechanisms, by Genliang Chen, Zhuang Zhang, Zhengtao Chen, Hao Wang -- A pure-inertia method for dynamic balancing of symmetric planar mechanisms, by Jan De Jong, Yuanqing Wu, Marco Carricato, Just Herder -- Stiffness and deformation of mechanisms with locally flexible bodies: a general method using expanded passive joints, by Gonzalo Moreno, Julio Frantz, Lauro Nicolazzi, Rodrigo de Souza Vieira, Daniel Martins -- Kinematic Characteristics of Parallel Continuum Mechanisms, by Oscar Altuzarra, Diego Caballero, Qiuchen Zhang, Francisco J. …”
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Interpretable Machine Learning with Python : Build Explainable, Fair, and Robust High-Performance Models with Hands-on, Real-world Examples. by Mas�is, Serg

Table of Contents: “…-- A business case for interpretability -- Better decisions -- More trusted brands -- More ethical -- More profitable -- Summary -- Image sources -- Dataset sources -- Further reading -- Chapter 2: Key Concepts of Interpretability -- Technical requirements -- The mission -- Details about CVD -- The approach -- Preparations -- Loading the libraries -- Understanding and preparing the data -- The data dictionary -- Data preparation -- Interpretation method types and scopes -- Model interpretability method types -- Model interpretability scopes -- Interpreting individual predictions with logistic regression -- Appreciating what hinders machine learning interpretability -- Non-linearity -- Interactivity -- Non-monotonicity -- Mission accomplished -- Summary -- Further reading -- Chapter 3: Interpretation Challenges -- Technical requirements -- The mission -- The approach -- The preparations -- Loading the libraries -- Understanding and preparing the data -- The data dictionary -- Data preparation -- Reviewing traditional model interpretation methods -- Predicting minutes delayed with various regression methods -- Classifying flights as delayed or not delayed with various classification methods -- Training and evaluating the classification models.…”
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Deep Learning with TensorFlow and Keras - 3rd Edition : Build and Deploy Supervised, Unsupervised, Deep, and Reinforcement Learning Models. by Kapoor, Amita

Table of Contents: “…Multiple and multivariate linear regression using the TensorFlow Keras API -- Classification tasks and decision boundaries -- Logistic regression -- Logistic regression on the MNIST dataset -- Summary -- References -- Chapter 3: Convolutional Neural Networks -- Deep convolutional neural networks -- Local receptive fields -- Shared weights and bias -- A mathematical example -- ConvNets in TensorFlow -- Pooling layers -- Max pooling -- Average pooling -- ConvNets summary -- An example of DCNN: LeNet -- LeNet code in TF -- Understanding the power of deep learning -- Recognizing CIFAR-10 images with deep learning -- Improving the CIFAR-10 performance with a deeper network -- Improving the CIFAR-10 performance with data augmentation -- Predicting with CIFAR-10 -- Very deep convolutional networks for large-scale image recognition -- Recognizing cats with a VGG16 net work -- Utilizing the tf.Keras built-in VGG16 net module -- Recycling pre-built deep learning models for extracting features -- Deep Inception V3 for transfer learning -- Other CNN architectures -- AlexNet -- Residual networks -- HighwayNets and DenseNets -- Xception -- Style transfer -- Content distance -- Style distance -- Summary -- References -- Chapter 4: Word Embeddings -- Word embedding ‒ origins and fundamentals -- Distributed representations -- Static embeddings -- Word2Vec -- GloVe -- Creating your own embeddings using Gensim -- Exploring the embedding space with Gensim -- Using word embeddings for spam detection -- Getting the data -- Making the data ready for use -- Building the embedding matrix -- Defining the spam classifier -- Training and evaluating the model -- Running the spam detector -- Neural embeddings - not just for words -- Item2Vec -- node2vec -- Character and subword embeddings -- Dynamic embeddings -- Sentence and paragraph embeddings.…”
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Intelligent and Fuzzy Techniques : Proceedings of the INFUS 2020 Conference, Istanbul, Turkey, July 21-23 2020. by Kahraman, Cengiz

Table of Contents: “…3 Determination of the Criteria -- 3.1 Economic Criteria (C1) -- 3.2 Quality Criteria (C2) -- 3.3 Social Criteria (C3) -- 3.4 Environmental Criteria (C4) -- 4 Spherical Fuzzy Analytic Hierarchy Process (SFAHP) -- 5 Prioritization of the Criteria -- 6 Conclusion -- References -- Estimation and Prediction -- Estimating Shopping Center Visitor Numbers Based on Various Environmental Indicators -- Abstract -- 1 Introduction -- 2 Background -- 3 Case Study -- 3.1 Data Sources -- 3.2 Data Models for Predictive Analysis -- 3.3 Data Models Evaluation -- 4 Discussion -- 5 Conclusion and Future Research -- References -- Predicting Customers' Next-to-Be Purchased Products -- Abstract -- 1 Introduction -- 2 Methodology -- 2.1 Data Preprocessing -- 2.2 Predictive Features -- 3 Conclusion -- References -- Estimating Breathing Levels of Asthma Patients with Artificial Intelligence Methods -- Abstract -- 1 Introduction -- 2 Method -- 2.1 Adaptive Neural Network Based Fuzzy Inference System (ANFIS) -- 2.2 Min-Max Normalization -- 2.3 Dataset Preparation -- 2.4 ANFIS Prediction Model -- 2.5 Artificial Neural Networks (ANN) -- 3 Results and Discussion -- 4 Conclusion -- References -- The Problem of Selection with the Fuzzy Axiomatic Design of MEMS Based Sensors in Industry 4.0 Predictive Maintenance Process -- Abstract -- 1 Introduction -- 2 Material and Method -- 2.1 Predictive Maintenance -- 3 MEMS - Micro Electro Mechanical Systems -- 4 Axiomatic Design -- 4.1 Introduction to Axiomatic Design Methodology -- 4.2 Axiomatic Design Method with Fuzzy Numbers -- 5 Application of Fuzzy Axiomatic Design on the MEMS -- 6 Conclusion and Future Work -- References -- Fuzzy Based Noise Removal, Age Group and Gender Prediction with CNN -- Abstract -- 1 Introduction -- 2 Face Recognition -- 3 Method -- 3.1 Fuzzy Logic -- 3.2 Convolutional Neural Network (CNN).…”
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Deep Learning with Python, Second Edition. by Chollet, Francois

Table of Contents: “…6.2.5 Regularize and tune your model -- 6.3 Deploy the model -- 6.3.1 Explain your work to stakeholders and set expectations -- 6.3.2 Ship an inference model -- 6.3.3 Monitor your model in the wild -- 6.3.4 Maintain your model -- Summary -- 7 Working with Keras: A deep dive -- 7.1 A spectrum of workflows -- 7.2 Different ways to build Keras models -- 7.2.1 The Sequential model -- 7.2.2 The Functional API -- 7.2.3 Subclassing the Model class -- 7.2.4 Mixing and matching different components -- 7.2.5 Remember: Use the right tool for the job -- 7.3 Using built-in training and evaluation loops -- 7.3.1 Writing your own metrics -- 7.3.2 Using callbacks -- 7.3.3 Writing your own callbacks -- 7.3.4 Monitoring and visualization with TensorBoard -- 7.4 Writing your own training and evaluation loops -- 7.4.1 Training versus inference -- 7.4.2 Low-level usage of metrics -- 7.4.3 A complete training and evaluation loop -- 7.4.4 Make it fast with tf.function -- 7.4.5 Leveraging fit() with a custom training loop -- Summary -- 8 Introduction to deep learning for computer vision -- 8.1 Introduction to convnets -- 8.1.1 The convolution operation -- 8.1.2 The max-pooling operation -- 8.2 Training a convnet from scratch on a small dataset -- 8.2.1 The relevance of deep learning for small-data problems -- 8.2.2 Downloading the data -- 8.2.3 Building the model -- 8.2.4 Data preprocessing -- 8.2.5 Using data augmentation -- 8.3 Leveraging a pretrained model -- 8.3.1 Feature extraction with a pretrained model -- 8.3.2 Fine-tuning a pretrained model -- Summary -- 9 Advanced deep learning for computer vision -- 9.1 Three essential computer vision tasks -- 9.2 An image segmentation example -- 9.3 Modern convnet architecture patterns -- 9.3.1 Modularity, hierarchy, and reuse -- 9.3.2 Residual connections -- 9.3.3 Batch normalization -- 9.3.4 Depthwise separable convolutions.…”
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Deep Learning : Foundations and Concepts. by Bishop, Christopher M.

Table of Contents: “…Object Detection -- 10.4.1 Bounding boxes -- 10.4.2 Intersection-over-union -- 10.4.3 Sliding windows -- 10.4.4 Detection across scales -- 10.4.5 Non-max suppression -- 10.4.6 Fast region CNNs -- 10.5. …”
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