Learning From Clusters

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Designing for Modern Learning

Author : Crystal Kadakia
Publisher : Association for Talent Development
ISBN 13 : 195049666X
Total Pages : 359 pages
Book Rating : 4.9/5 (54 download)

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Book Synopsis Designing for Modern Learning by : Crystal Kadakia

Download or read book Designing for Modern Learning written by Crystal Kadakia and published by Association for Talent Development. This book was released on 2020-06-30 with total page 359 pages. Available in PDF, EPUB and Kindle. Book excerpt: Meet Learning Needs With New Tools and New Thinking Learning is no longer an activity or luxury that only occurs at specific stages in your life or career. With the digital revolution, learning has become immediate, real-time, and relevant whether you’re young, old, in the workforce, in school, or at home. As a learning and development professional, you’ve likely confronted the digital learning revolution armed with instructional design models from the pre-digital world. But today’s digital universe has a new model to address its wealth of new technologies and a new philosophy of learning experience design: learning cluster design. Designing for Modern Learning: Beyond ADDIE and SAM offers you and your learners a new way to learn. It describes the fundamental shift that has occurred in the nature of L&D’s role as a result of the digital revolution and introduces a new five-step model: the Owens-Kadakia Learning Cluster Design Model (OK-LCD Model), a new five-step model for training design that meets the needs of modern learning. The model’s five steps or actions are an easy-to-follow mnemonic, CLUSTER: Change on-the-job behavior Learn learner-to-learner differences Upgrade existing assets Surround learning with meaningful assets Track transformation of Everyone’s Results. In each chapter, the authors share stories of business leaders, L&D professionals, and learners who have successfully adopted the OK-LCD Model, detailing how they altered organizational mindsets to meet the needs of modern learners and their organizations. Included are how-to features, tools, tips, and real-life “in practice” sections. This is an exciting time to be in L&D. It’s time to join the revolution.

Enrichment Clusters

Author : Joseph S. Renzulli
Publisher : Routledge
ISBN 13 : 1000492745
Total Pages : 155 pages
Book Rating : 4.0/5 (4 download)

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Book Synopsis Enrichment Clusters by : Joseph S. Renzulli

Download or read book Enrichment Clusters written by Joseph S. Renzulli and published by Routledge. This book was released on 2021-09-03 with total page 155 pages. Available in PDF, EPUB and Kindle. Book excerpt: Enrichment clusters engage students and facilitators in student-driven, real-world learning experiences. Grouped by interest, students working like practicing professionals apply advanced content and methods to develop products and services for authentic audiences. Clusters are scheduled during the school day over an extended period of time and involve all students. This updated second edition of Enrichment Clusters provides the rationale for including this important enrichment program for all students, suggestions for creating buy-in, and a step-by-step guide for successful implementation of a self-sustaining enrichment cluster program within the context of specific schools. Included are staff development activities, suggestions for evaluation and program improvement, guidelines for developing high quality cluster experiences for teachers and students, suggested resources, and everything one needs to develop, implement, and sustain a top-quality enrichment cluster program.

Foundations of the Knowledge Economy

Author : Knut Ingar Westeren
Publisher : Edward Elgar Publishing
ISBN 13 : 0857937723
Total Pages : 297 pages
Book Rating : 4.8/5 (579 download)

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Book Synopsis Foundations of the Knowledge Economy by : Knut Ingar Westeren

Download or read book Foundations of the Knowledge Economy written by Knut Ingar Westeren and published by Edward Elgar Publishing. This book was released on 2012-01-01 with total page 297 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents new evidence concerning the influential role of context and institutions on the relations between knowledge, innovation, clusters and learning. From a truly international perspective, the expert contributors capture the most interesting and relevant aspects of knowledge economy. They explore an evolutionary explanation of how culture can play a significant role in learning and the development of skills. Presenting new data and theory developments, this insightful book reveals how changes in the dynamics of knowledge influence the circumstances under which innovation occurs. It also examines cluster development in the knowledge economy, from regional to virtual space. This volume will prove invaluable to academics and researchers who are interested in exploring new ideas surrounding the knowledge economy. Those employed in consultant firms and the public sector, where an understanding of the knowledge economy is important, will also find plenty of relevant information in this enriching compendium.

Mastering Machine Learning Algorithms

Author : Giuseppe Bonaccorso
Publisher : Packt Publishing Ltd
ISBN 13 : 1788625900
Total Pages : 567 pages
Book Rating : 4.7/5 (886 download)

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Book Synopsis Mastering Machine Learning Algorithms by : Giuseppe Bonaccorso

Download or read book Mastering Machine Learning Algorithms written by Giuseppe Bonaccorso and published by Packt Publishing Ltd. This book was released on 2018-05-25 with total page 567 pages. Available in PDF, EPUB and Kindle. Book excerpt: Explore and master the most important algorithms for solving complex machine learning problems. Key Features Discover high-performing machine learning algorithms and understand how they work in depth. One-stop solution to mastering supervised, unsupervised, and semi-supervised machine learning algorithms and their implementation. Master concepts related to algorithm tuning, parameter optimization, and more Book Description Machine learning is a subset of AI that aims to make modern-day computer systems smarter and more intelligent. The real power of machine learning resides in its algorithms, which make even the most difficult things capable of being handled by machines. However, with the advancement in the technology and requirements of data, machines will have to be smarter than they are today to meet the overwhelming data needs; mastering these algorithms and using them optimally is the need of the hour. Mastering Machine Learning Algorithms is your complete guide to quickly getting to grips with popular machine learning algorithms. You will be introduced to the most widely used algorithms in supervised, unsupervised, and semi-supervised machine learning, and will learn how to use them in the best possible manner. Ranging from Bayesian models to the MCMC algorithm to Hidden Markov models, this book will teach you how to extract features from your dataset and perform dimensionality reduction by making use of Python-based libraries such as scikit-learn. You will also learn how to use Keras and TensorFlow to train effective neural networks. If you are looking for a single resource to study, implement, and solve end-to-end machine learning problems and use-cases, this is the book you need. What you will learn Explore how a ML model can be trained, optimized, and evaluated Understand how to create and learn static and dynamic probabilistic models Successfully cluster high-dimensional data and evaluate model accuracy Discover how artificial neural networks work and how to train, optimize, and validate them Work with Autoencoders and Generative Adversarial Networks Apply label spreading and propagation to large datasets Explore the most important Reinforcement Learning techniques Who this book is for This book is an ideal and relevant source of content for data science professionals who want to delve into complex machine learning algorithms, calibrate models, and improve the predictions of the trained model. A basic knowledge of machine learning is preferred to get the best out of this guide.

Clustering

Author : Rui Xu
Publisher : John Wiley & Sons
ISBN 13 : 0470382783
Total Pages : 400 pages
Book Rating : 4.4/5 (73 download)

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Book Synopsis Clustering by : Rui Xu

Download or read book Clustering written by Rui Xu and published by John Wiley & Sons. This book was released on 2008-11-03 with total page 400 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is the first book to take a truly comprehensive look at clustering. It begins with an introduction to cluster analysis and goes on to explore: proximity measures; hierarchical clustering; partition clustering; neural network-based clustering; kernel-based clustering; sequential data clustering; large-scale data clustering; data visualization and high-dimensional data clustering; and cluster validation. The authors assume no previous background in clustering and their generous inclusion of examples and references help make the subject matter comprehensible for readers of varying levels and backgrounds.

Graph Representation Learning

Author : William L. William L. Hamilton
Publisher : Springer Nature
ISBN 13 : 3031015886
Total Pages : 141 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis Graph Representation Learning by : William L. William L. Hamilton

Download or read book Graph Representation Learning written by William L. William L. Hamilton and published by Springer Nature. This book was released on 2022-06-01 with total page 141 pages. Available in PDF, EPUB and Kindle. Book excerpt: Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial for creating systems that can learn, reason, and generalize from this kind of data. Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of convolutional neural networks to graph-structured data, and neural message-passing approaches inspired by belief propagation. These advances in graph representation learning have led to new state-of-the-art results in numerous domains, including chemical synthesis, 3D vision, recommender systems, question answering, and social network analysis. This book provides a synthesis and overview of graph representation learning. It begins with a discussion of the goals of graph representation learning as well as key methodological foundations in graph theory and network analysis. Following this, the book introduces and reviews methods for learning node embeddings, including random-walk-based methods and applications to knowledge graphs. It then provides a technical synthesis and introduction to the highly successful graph neural network (GNN) formalism, which has become a dominant and fast-growing paradigm for deep learning with graph data. The book concludes with a synthesis of recent advancements in deep generative models for graphs—a nascent but quickly growing subset of graph representation learning.

Statistical Data Analysis Based on the L1-Norm and Related Methods

Author : Yadolah Dodge
Publisher : Birkhäuser
ISBN 13 : 3034882017
Total Pages : 447 pages
Book Rating : 4.0/5 (348 download)

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Book Synopsis Statistical Data Analysis Based on the L1-Norm and Related Methods by : Yadolah Dodge

Download or read book Statistical Data Analysis Based on the L1-Norm and Related Methods written by Yadolah Dodge and published by Birkhäuser. This book was released on 2012-12-06 with total page 447 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume contains a selection of invited papers, presented to the fourth International Conference on Statistical Data Analysis Based on the L1-Norm and Related Methods, held in Neuchâtel, Switzerland, from August 4–9, 2002. The contributions represent clear evidence to the importance of the development of theory, methods and applications related to the statistical data analysis based on the L1-norm.

Machine Learning Fundamentals

Author : Hyatt Saleh
Publisher : Packt Publishing Ltd
ISBN 13 : 1789801761
Total Pages : 240 pages
Book Rating : 4.7/5 (898 download)

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Book Synopsis Machine Learning Fundamentals by : Hyatt Saleh

Download or read book Machine Learning Fundamentals written by Hyatt Saleh and published by Packt Publishing Ltd. This book was released on 2018-11-29 with total page 240 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the flexibility and features of scikit-learn and Python, build machine learning algorithms that optimize the programming process and take application performance to a whole new level Key FeaturesExplore scikit-learn uniform API and its application into any type of modelUnderstand the difference between supervised and unsupervised modelsLearn the usage of machine learning through real-world examplesBook Description As machine learning algorithms become popular, new tools that optimize these algorithms are also developed. Machine Learning Fundamentals explains you how to use the syntax of scikit-learn. You'll study the difference between supervised and unsupervised models, as well as the importance of choosing the appropriate algorithm for each dataset. You'll apply unsupervised clustering algorithms over real-world datasets, to discover patterns and profiles, and explore the process to solve an unsupervised machine learning problem. The focus of the book then shifts to supervised learning algorithms. You'll learn to implement different supervised algorithms and develop neural network structures using the scikit-learn package. You'll also learn how to perform coherent result analysis to improve the performance of the algorithm by tuning hyperparameters. By the end of this book, you will have gain all the skills required to start programming machine learning algorithms. What you will learnUnderstand the importance of data representationGain insights into the differences between supervised and unsupervised modelsExplore data using the Matplotlib libraryStudy popular algorithms, such as k-means, Mean-Shift, and DBSCANMeasure model performance through different metricsImplement a confusion matrix using scikit-learnStudy popular algorithms, such as Naïve-Bayes, Decision Tree, and SVMPerform error analysis to improve the performance of the modelLearn to build a comprehensive machine learning programWho this book is for Machine Learning Fundamentals is designed for developers who are new to the field of machine learning and want to learn how to use the scikit-learn library to develop machine learning algorithms. You must have some knowledge and experience in Python programming, but you do not need any prior knowledge of scikit-learn or machine learning algorithms.

Multiple Classifier Systems

Author : Carlo Sansone
Publisher : Springer Science & Business Media
ISBN 13 : 3642215564
Total Pages : 382 pages
Book Rating : 4.6/5 (422 download)

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Book Synopsis Multiple Classifier Systems by : Carlo Sansone

Download or read book Multiple Classifier Systems written by Carlo Sansone and published by Springer Science & Business Media. This book was released on 2011-06-14 with total page 382 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 10th International Workshop on Multiple Classifier Systems, MCS 2011, held in Naples, Italy, in June 2011. The 36 revised papers presented together with two invited papers were carefully reviewed and selected from more than 50 submissions. The contributions are organized into sessions dealing with classifier ensembles; trees and forests; one-class classifiers; multiple kernels; classifier selection; sequential combination; ECOC; diversity; clustering; biometrics; and computer security.

Clustering Algorithms

Author : John A. Hartigan
Publisher : John Wiley & Sons
ISBN 13 :
Total Pages : 374 pages
Book Rating : 4.3/5 (91 download)

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Book Synopsis Clustering Algorithms by : John A. Hartigan

Download or read book Clustering Algorithms written by John A. Hartigan and published by John Wiley & Sons. This book was released on 1975 with total page 374 pages. Available in PDF, EPUB and Kindle. Book excerpt: Shows how Galileo, Newton, and Einstein tried to explain gravity. Discusses the concept of microgravity and NASA's research on gravity and microgravity.

Linux Clustering

Author : Charles Bookman
Publisher : Sams Publishing
ISBN 13 : 9781578702749
Total Pages : 296 pages
Book Rating : 4.7/5 (27 download)

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Book Synopsis Linux Clustering by : Charles Bookman

Download or read book Linux Clustering written by Charles Bookman and published by Sams Publishing. This book was released on 2003 with total page 296 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Linux Clustering" is the premier resource for system administrators wishing to implement clustering solutions on the many types of Linux systems. It guides Linux Administrators through difficult tasks while offering helpful tips and tricks.

Semi-Supervised Learning

Author : Olivier Chapelle
Publisher : MIT Press
ISBN 13 : 0262514125
Total Pages : 525 pages
Book Rating : 4.2/5 (625 download)

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Book Synopsis Semi-Supervised Learning by : Olivier Chapelle

Download or read book Semi-Supervised Learning written by Olivier Chapelle and published by MIT Press. This book was released on 2010-01-22 with total page 525 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive review of an area of machine learning that deals with the use of unlabeled data in classification problems: state-of-the-art algorithms, a taxonomy of the field, applications, benchmark experiments, and directions for future research. In the field of machine learning, semi-supervised learning (SSL) occupies the middle ground, between supervised learning (in which all training examples are labeled) and unsupervised learning (in which no label data are given). Interest in SSL has increased in recent years, particularly because of application domains in which unlabeled data are plentiful, such as images, text, and bioinformatics. This first comprehensive overview of SSL presents state-of-the-art algorithms, a taxonomy of the field, selected applications, benchmark experiments, and perspectives on ongoing and future research.Semi-Supervised Learning first presents the key assumptions and ideas underlying the field: smoothness, cluster or low-density separation, manifold structure, and transduction. The core of the book is the presentation of SSL methods, organized according to algorithmic strategies. After an examination of generative models, the book describes algorithms that implement the low-density separation assumption, graph-based methods, and algorithms that perform two-step learning. The book then discusses SSL applications and offers guidelines for SSL practitioners by analyzing the results of extensive benchmark experiments. Finally, the book looks at interesting directions for SSL research. The book closes with a discussion of the relationship between semi-supervised learning and transduction.

The Life Cycle of Clusters

Author : Dirk Fornahl
Publisher : Edward Elgar Publishing
ISBN 13 : 1784719285
Total Pages : 321 pages
Book Rating : 4.7/5 (847 download)

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Book Synopsis The Life Cycle of Clusters by : Dirk Fornahl

Download or read book The Life Cycle of Clusters written by Dirk Fornahl and published by Edward Elgar Publishing. This book was released on 2017-04-28 with total page 321 pages. Available in PDF, EPUB and Kindle. Book excerpt: One-size-fits-all cluster policies have been rightly criticized in the literature. One promising approach is to focus cluster policies on the specific needs of firms depending on the stage of development (emergence, growth, sustainment or decline) their cluster is in. In this highly insightful book, these stage-specific cluster policies are analysed and evaluated. Moreover, several chapters also focus on smart specialization policies to promote regional development by taking into account the emergence and adaptation of clusters and industries.

Innovative Clusters Drivers of National Innovation Systems

Author : OECD
Publisher : OECD Publishing
ISBN 13 : 9264193383
Total Pages : 405 pages
Book Rating : 4.2/5 (641 download)

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Book Synopsis Innovative Clusters Drivers of National Innovation Systems by : OECD

Download or read book Innovative Clusters Drivers of National Innovation Systems written by OECD and published by OECD Publishing. This book was released on 2001-06-11 with total page 405 pages. Available in PDF, EPUB and Kindle. Book excerpt: Policies to stimulate innovation at national and local levels must both build on and contribute to the dynamics of innovative clusters. This book presents a series of papers written by policy makers and academic experts in the field, that demonstrate why and how this can be done.

Cluster Analysis and Data Mining

Author : Ronald S. King
Publisher : Mercury Learning and Information
ISBN 13 : 1942270135
Total Pages : 363 pages
Book Rating : 4.9/5 (422 download)

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Book Synopsis Cluster Analysis and Data Mining by : Ronald S. King

Download or read book Cluster Analysis and Data Mining written by Ronald S. King and published by Mercury Learning and Information. This book was released on 2015-05-12 with total page 363 pages. Available in PDF, EPUB and Kindle. Book excerpt: Cluster analysis is used in data mining and is a common technique for statistical data analysis used in many fields of study, such as the medical & life sciences, behavioral & social sciences, engineering, and in computer science. Designed for training industry professionals or for a course on clustering and classification, it can also be used as a companion text for applied statistics. No previous experience in clustering or data mining is assumed. Informal algorithms for clustering data and interpreting results are emphasized. In order to evaluate the results of clustering and to explore data, graphical methods and data structures are used for representing data. Throughout the text, examples and references are provided, in order to enable the material to be comprehensible for a diverse audience. A companion disc includes numerous appendices with programs, data, charts, solutions, etc. eBook Customers: Companion files are available for downloading with order number/proof of purchase by writing to the publisher at [email protected]. FEATURES *Places emphasis on illustrating the underlying logic in making decisions during the cluster analysis *Discusses the related applications of statistic, e.g., Ward’s method (ANOVA), JAN (regression analysis & correlational analysis), cluster validation (hypothesis testing, goodness-of-fit, Monte Carlo simulation, etc.) *Contains separate chapters on JAN and the clustering of categorical data *Includes a companion disc with solutions to exercises, programs, data sets, charts, etc.

Model-Based Clustering and Classification for Data Science

Author : Charles Bouveyron
Publisher : Cambridge University Press
ISBN 13 : 1108640591
Total Pages : 447 pages
Book Rating : 4.1/5 (86 download)

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Book Synopsis Model-Based Clustering and Classification for Data Science by : Charles Bouveyron

Download or read book Model-Based Clustering and Classification for Data Science written by Charles Bouveyron and published by Cambridge University Press. This book was released on 2019-07-25 with total page 447 pages. Available in PDF, EPUB and Kindle. Book excerpt: Cluster analysis finds groups in data automatically. Most methods have been heuristic and leave open such central questions as: how many clusters are there? Which method should I use? How should I handle outliers? Classification assigns new observations to groups given previously classified observations, and also has open questions about parameter tuning, robustness and uncertainty assessment. This book frames cluster analysis and classification in terms of statistical models, thus yielding principled estimation, testing and prediction methods, and sound answers to the central questions. It builds the basic ideas in an accessible but rigorous way, with extensive data examples and R code; describes modern approaches to high-dimensional data and networks; and explains such recent advances as Bayesian regularization, non-Gaussian model-based clustering, cluster merging, variable selection, semi-supervised and robust classification, clustering of functional data, text and images, and co-clustering. Written for advanced undergraduates in data science, as well as researchers and practitioners, it assumes basic knowledge of multivariate calculus, linear algebra, probability and statistics.

Cluster Computing for Robotics and Computer Vision

Author : Damian M. Lyons
Publisher : World Scientific
ISBN 13 : 9812836357
Total Pages : 235 pages
Book Rating : 4.8/5 (128 download)

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Book Synopsis Cluster Computing for Robotics and Computer Vision by : Damian M. Lyons

Download or read book Cluster Computing for Robotics and Computer Vision written by Damian M. Lyons and published by World Scientific. This book was released on 2011 with total page 235 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this book, we look at how cluster technology can be leveraged to build better robots. Algorithms and approaches in key areas of robotics and computer vision, such as map building, path planning, target tracking, action selection and learning, are reviewed and cluster implementations for these are presented. The objective of the book is to give professionals working in the beowulf cluster or robotics and computer vision fields a concrete view of the strong synergy between the areas as well as to spur further fruitful exploitation of this connection. The book is written at a level appropriate for an advanced undergraduate or graduate student. The key concepts in robotics, computer vision and cluster computing are introduced before being used to make the text useful to a wide audience in these fields.