Optimizing Data To Learning To Action

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Optimizing Data-to-Learning-to-Action

Author : Steven Flinn
Publisher : Apress
ISBN 13 : 1484235312
Total Pages : 201 pages
Book Rating : 4.4/5 (842 download)

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Book Synopsis Optimizing Data-to-Learning-to-Action by : Steven Flinn

Download or read book Optimizing Data-to-Learning-to-Action written by Steven Flinn and published by Apress. This book was released on 2018-04-06 with total page 201 pages. Available in PDF, EPUB and Kindle. Book excerpt: Apply a powerful new approach and method that ensures continuous performance improvement for your business. You will learn how to determine and value the people, process, and technology-based solutions that will optimize your organization’s data-to-learning-to-action processes. This book describes in detail how to holistically optimize the chain of activities that span from data to learning to decisions to actions, an imperative for achieving outstanding performance in today’s business environment. Adapting and integrating insights from decision science, constraint theory, and process improvement, the book provides a method that is clear, effective, and can be applied to nearly every business function and sector. You will learn how to systematically work backwards from decisions to data, estimate the flow of value along the chain, and identify the inevitable value bottlenecks. And, importantly, you will learn techniques for quantifying the value that can be attained by successfully addressing the bottlenecks, providing the credible support needed to make the right level of investments at the right place and at just the right time. In today’s dynamic environment, with its never-ending stream of new, disruptive technologies that executives must consider (e.g., cloud computing, Internet of Things, AI/machine learning, business intelligence, enterprise social, etc., along with the associated big data generated), author Steven Flinn provides the comprehensive approach that is needed for making effective decisions about these technologies, underpinned by credibly quantified value. What You’ll Learn Understand data-to-learning-to-action processes and their fundamental elements Discover the highest leverage data-to-learning-to-action processes in your organization Identify the key decisions that are associated with a data-to-learning-to-action process Know why it’s NOT all about data, but it IS all about decisions and learning Determine the value upside of enhanced learning that can improve decisions Work backwards from the decisions to determine the value constraints in data-to-learning-to-action processes Evaluate people, process, and technology-based solution options to address the constraints Quantify the expected value of each of the solution options and prioritize accordingly Implement, measure, and continuously improve by addressing the next constraints on value Who This Book Is For Business executives and managers seeking the next level of organizational performance, knowledge workers who want to maximize their impact, technology managers and practitioners who require a more effective means to prioritize technology options and deployments, technology providers who need a way to credibly quantify the value of their offerings, and consultants who are ready to build practices around the next big business performance paradigm

Optimization for Data Analysis

Author : Stephen J. Wright
Publisher : Cambridge University Press
ISBN 13 : 1316518981
Total Pages : 239 pages
Book Rating : 4.3/5 (165 download)

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Book Synopsis Optimization for Data Analysis by : Stephen J. Wright

Download or read book Optimization for Data Analysis written by Stephen J. Wright and published by Cambridge University Press. This book was released on 2022-04-21 with total page 239 pages. Available in PDF, EPUB and Kindle. Book excerpt: A concise text that presents and analyzes the fundamental techniques and methods in optimization that are useful in data science.

Optimizing the Power of Action Learning

Author :
Publisher :
ISBN 13 :
Total Pages : 5 pages
Book Rating : 4.:/5 (17 download)

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Book Synopsis Optimizing the Power of Action Learning by :

Download or read book Optimizing the Power of Action Learning written by and published by . This book was released on 2017 with total page 5 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Machine Learning, Optimization, and Data Science

Author : Giuseppe Nicosia
Publisher : Springer Nature
ISBN 13 : 3030954706
Total Pages : 571 pages
Book Rating : 4.0/5 (39 download)

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Book Synopsis Machine Learning, Optimization, and Data Science by : Giuseppe Nicosia

Download or read book Machine Learning, Optimization, and Data Science written by Giuseppe Nicosia and published by Springer Nature. This book was released on 2022-02-01 with total page 571 pages. Available in PDF, EPUB and Kindle. Book excerpt: This two-volume set, LNCS 13163-13164, constitutes the refereed proceedings of the 7th International Conference on Machine Learning, Optimization, and Data Science, LOD 2021, together with the first edition of the Symposium on Artificial Intelligence and Neuroscience, ACAIN 2021. The total of 86 full papers presented in this two-volume post-conference proceedings set was carefully reviewed and selected from 215 submissions. These research articles were written by leading scientists in the fields of machine learning, artificial intelligence, reinforcement learning, computational optimization, neuroscience, and data science presenting a substantial array of ideas, technologies, algorithms, methods, and applications.

Optimization and Data Science: Trends and Applications

Author : Adriano Masone
Publisher : Springer Nature
ISBN 13 : 3030862860
Total Pages : 189 pages
Book Rating : 4.0/5 (38 download)

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Book Synopsis Optimization and Data Science: Trends and Applications by : Adriano Masone

Download or read book Optimization and Data Science: Trends and Applications written by Adriano Masone and published by Springer Nature. This book was released on 2022-01-01 with total page 189 pages. Available in PDF, EPUB and Kindle. Book excerpt: This proceedings volume collects contributions from the 5th AIRO Young Workshop and AIRO PhD School 2021 joint event on “Optimization and Data Science: Trends and Applications”, held online, from February 8 to 12, 2021. The joint event was organized by AIROYoung representatives and the Operations Research Group of the Department of Electrical Engineering and Information Technology of the University “Federico II” of Naples. The selected contributions represent the state-of-the-art knowledge related to different branches of research, such as data science, machine learning and combinatorial optimization. Therefore, this book is primarily addressed to researchers and PhD students of the operations research community. However, due to its interdisciplinary content, it will be of high interest for other closely related research communities. Moreover, this volume not only presents theoretical results but also covers real applications in computer science, engineering, economics, healthcare, and logistics, making it interesting for practitioners facing complex decision-making problems in these areas.

Artificial Intelligence for Business Optimization

Author : Bhuvan Unhelkar
Publisher : CRC Press
ISBN 13 : 1000409430
Total Pages : 325 pages
Book Rating : 4.0/5 (4 download)

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Book Synopsis Artificial Intelligence for Business Optimization by : Bhuvan Unhelkar

Download or read book Artificial Intelligence for Business Optimization written by Bhuvan Unhelkar and published by CRC Press. This book was released on 2021-08-09 with total page 325 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book explains how AI and Machine Learning can be applied to help businesses solve problems, support critical thinking and ultimately create customer value and increase profit. By considering business strategies, business process modeling, quality assurance, cybersecurity, governance and big data and focusing on functions, processes, and people’s behaviors it helps businesses take a truly holistic approach to business optimization. It contains practical examples that make it easy to understand the concepts and apply them. It is written for practitioners (consultants, senior executives, decision-makers) dealing with real-life business problems on a daily basis, who are keen to develop systematic strategies for the application of AI/ML/BD technologies to business automation and optimization, as well as researchers who want to explore the industrial applications of AI and higher-level students.

Handbook of Research on Innovative Techniques, Trends, and Analysis for Optimized Research Methods

Author : Wang, Victor C.X.
Publisher : IGI Global
ISBN 13 : 1522551654
Total Pages : 472 pages
Book Rating : 4.5/5 (225 download)

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Book Synopsis Handbook of Research on Innovative Techniques, Trends, and Analysis for Optimized Research Methods by : Wang, Victor C.X.

Download or read book Handbook of Research on Innovative Techniques, Trends, and Analysis for Optimized Research Methods written by Wang, Victor C.X. and published by IGI Global. This book was released on 2017-12-30 with total page 472 pages. Available in PDF, EPUB and Kindle. Book excerpt: Information acquisition and management has always had a profound impact on societal and organizational progression. This is due to higher education programs continuously expanding, students and academics being engaged in modern research, and the constant evaluating of current processes in education for optimization for the future. The Handbook of Research on Innovative Techniques, Trends, and Analysis for Optimized Research Methods is a comprehensive reference source focused on the latest research methods currently facing educational technology and learners. While highlighting the innovative trends and methods, readers will learn valuable ways to conduct research and advance the understanding of ideas based on the results of their research. This publication is an important asset for teachers, researchers, practitioners, and graduate students looking to gain more knowledge on research trends and their applications.

Machine Learning in Action

Author : Peter Harrington
Publisher : Simon and Schuster
ISBN 13 : 1638352453
Total Pages : 558 pages
Book Rating : 4.6/5 (383 download)

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Book Synopsis Machine Learning in Action by : Peter Harrington

Download or read book Machine Learning in Action written by Peter Harrington and published by Simon and Schuster. This book was released on 2012-04-03 with total page 558 pages. Available in PDF, EPUB and Kindle. Book excerpt: Summary Machine Learning in Action is unique book that blends the foundational theories of machine learning with the practical realities of building tools for everyday data analysis. You'll use the flexible Python programming language to build programs that implement algorithms for data classification, forecasting, recommendations, and higher-level features like summarization and simplification. About the Book A machine is said to learn when its performance improves with experience. Learning requires algorithms and programs that capture data and ferret out the interestingor useful patterns. Once the specialized domain of analysts and mathematicians, machine learning is becoming a skill needed by many. Machine Learning in Action is a clearly written tutorial for developers. It avoids academic language and takes you straight to the techniques you'll use in your day-to-day work. Many (Python) examples present the core algorithms of statistical data processing, data analysis, and data visualization in code you can reuse. You'll understand the concepts and how they fit in with tactical tasks like classification, forecasting, recommendations, and higher-level features like summarization and simplification. Readers need no prior experience with machine learning or statistical processing. Familiarity with Python is helpful. Purchase of the print book comes with an offer of a free PDF, ePub, and Kindle eBook from Manning. Also available is all code from the book. What's Inside A no-nonsense introduction Examples showing common ML tasks Everyday data analysis Implementing classic algorithms like Apriori and Adaboos Table of Contents PART 1 CLASSIFICATION Machine learning basics Classifying with k-Nearest Neighbors Splitting datasets one feature at a time: decision trees Classifying with probability theory: naïve Bayes Logistic regression Support vector machines Improving classification with the AdaBoost meta algorithm PART 2 FORECASTING NUMERIC VALUES WITH REGRESSION Predicting numeric values: regression Tree-based regression PART 3 UNSUPERVISED LEARNING Grouping unlabeled items using k-means clustering Association analysis with the Apriori algorithm Efficiently finding frequent itemsets with FP-growth PART 4 ADDITIONAL TOOLS Using principal component analysis to simplify data Simplifying data with the singular value decomposition Big data and MapReduce

The Elements of Joint Learning and Optimization in Operations Management

Author : Xi Chen
Publisher : Springer Nature
ISBN 13 : 3031019261
Total Pages : 444 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis The Elements of Joint Learning and Optimization in Operations Management by : Xi Chen

Download or read book The Elements of Joint Learning and Optimization in Operations Management written by Xi Chen and published by Springer Nature. This book was released on 2022-09-20 with total page 444 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book examines recent developments in Operations Management, and focuses on four major application areas: dynamic pricing, assortment optimization, supply chain and inventory management, and healthcare operations. Data-driven optimization in which real-time input of data is being used to simultaneously learn the (true) underlying model of a system and optimize its performance, is becoming increasingly important in the last few years, especially with the rise of Big Data.

Machine Learning, Optimization, and Big Data

Author : Panos M. Pardalos
Publisher : Springer
ISBN 13 : 3319514695
Total Pages : 475 pages
Book Rating : 4.3/5 (195 download)

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Book Synopsis Machine Learning, Optimization, and Big Data by : Panos M. Pardalos

Download or read book Machine Learning, Optimization, and Big Data written by Panos M. Pardalos and published by Springer. This book was released on 2016-12-24 with total page 475 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes revised selected papers from the Second International Workshop on Machine Learning, Optimization, and Big Data, MOD 2016, held in Volterra, Italy, in August 2016. The 40 papers presented in this volume were carefully reviewed and selected from 97 submissions. These proceedings contain papers in the fields of Machine Learning, Computational Optimization and DataScience presenting a substantial array of ideas, technologies, algorithms, methods and applications.

Bayesian Optimization and Data Science

Author : Francesco Archetti
Publisher : Springer Nature
ISBN 13 : 3030244946
Total Pages : 137 pages
Book Rating : 4.0/5 (32 download)

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Book Synopsis Bayesian Optimization and Data Science by : Francesco Archetti

Download or read book Bayesian Optimization and Data Science written by Francesco Archetti and published by Springer Nature. This book was released on 2019-09-25 with total page 137 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume brings together the main results in the field of Bayesian Optimization (BO), focusing on the last ten years and showing how, on the basic framework, new methods have been specialized to solve emerging problems from machine learning, artificial intelligence, and system optimization. It also analyzes the software resources available for BO and a few selected application areas. Some areas for which new results are shown include constrained optimization, safe optimization, and applied mathematics, specifically BO's use in solving difficult nonlinear mixed integer problems. The book will help bring readers to a full understanding of the basic Bayesian Optimization framework and gain an appreciation of its potential for emerging application areas. It will be of particular interest to the data science, computer science, optimization, and engineering communities.

Deep Learning Techniques and Optimization Strategies in Big Data Analytics

Author : Thomas, J. Joshua
Publisher : IGI Global
ISBN 13 : 1799811948
Total Pages : 355 pages
Book Rating : 4.7/5 (998 download)

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Book Synopsis Deep Learning Techniques and Optimization Strategies in Big Data Analytics by : Thomas, J. Joshua

Download or read book Deep Learning Techniques and Optimization Strategies in Big Data Analytics written by Thomas, J. Joshua and published by IGI Global. This book was released on 2019-11-29 with total page 355 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many approaches have sprouted from artificial intelligence (AI) and produced major breakthroughs in the computer science and engineering industries. Deep learning is a method that is transforming the world of data and analytics. Optimization of this new approach is still unclear, however, and there’s a need for research on the various applications and techniques of deep learning in the field of computing. Deep Learning Techniques and Optimization Strategies in Big Data Analytics is a collection of innovative research on the methods and applications of deep learning strategies in the fields of computer science and information systems. While highlighting topics including data integration, computational modeling, and scheduling systems, this book is ideally designed for engineers, IT specialists, data analysts, data scientists, engineers, researchers, academicians, and students seeking current research on deep learning methods and its application in the digital industry.

Data-driven Modeling and Optimization: Applications to Social Computing

Author : Chao Gao
Publisher : Frontiers Media SA
ISBN 13 : 2889769607
Total Pages : 252 pages
Book Rating : 4.8/5 (897 download)

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Book Synopsis Data-driven Modeling and Optimization: Applications to Social Computing by : Chao Gao

Download or read book Data-driven Modeling and Optimization: Applications to Social Computing written by Chao Gao and published by Frontiers Media SA. This book was released on 2022-09-14 with total page 252 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Databricks ML in Action

Author : Stephanie Rivera
Publisher : Packt Publishing Ltd
ISBN 13 : 1800564007
Total Pages : 280 pages
Book Rating : 4.8/5 (5 download)

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Book Synopsis Databricks ML in Action by : Stephanie Rivera

Download or read book Databricks ML in Action written by Stephanie Rivera and published by Packt Publishing Ltd. This book was released on 2024-05-17 with total page 280 pages. Available in PDF, EPUB and Kindle. Book excerpt: Get to grips with autogenerating code, deploying ML algorithms, and leveraging various ML lifecycle features on the Databricks Platform, guided by best practices and reusable code for you to try, alter, and build on Key Features Build machine learning solutions faster than peers only using documentation Enhance or refine your expertise with tribal knowledge and concise explanations Follow along with code projects provided in GitHub to accelerate your projects Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionDiscover what makes the Databricks Data Intelligence Platform the go-to choice for top-tier machine learning solutions. Written by a team of industry experts at Databricks with decades of combined experience in big data, machine learning, and data science, Databricks ML in Action presents cloud-agnostic, end-to-end examples with hands-on illustrations of executing data science, machine learning, and generative AI projects on the Databricks Platform. You’ll develop expertise in Databricks' managed MLflow, Vector Search, AutoML, Unity Catalog, and Model Serving as you learn to apply them practically in everyday workflows. This Databricks book not only offers detailed code explanations but also facilitates seamless code importation for practical use. You’ll discover how to leverage the open-source Databricks platform to enhance learning, boost skills, and elevate productivity with supplemental resources. By the end of this book, you'll have mastered the use of Databricks for data science, machine learning, and generative AI, enabling you to deliver outstanding data products.What you will learn Set up a workspace for a data team planning to perform data science Monitor data quality and detect drift Use autogenerated code for ML modeling and data exploration Operationalize ML with feature engineering client, AutoML, VectorSearch, Delta Live Tables, AutoLoader, and Workflows Integrate open-source and third-party applications, such as OpenAI's ChatGPT, into your AI projects Communicate insights through Databricks SQL dashboards and Delta Sharing Explore data and models through the Databricks marketplace Who this book is for This book is for machine learning engineers, data scientists, and technical managers seeking hands-on expertise in implementing and leveraging the Databricks Data Intelligence Platform and its Lakehouse architecture to create data products.

Perturbations, Optimization, and Statistics

Author : Tamir Hazan
Publisher : MIT Press
ISBN 13 : 0262549948
Total Pages : 413 pages
Book Rating : 4.2/5 (625 download)

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Book Synopsis Perturbations, Optimization, and Statistics by : Tamir Hazan

Download or read book Perturbations, Optimization, and Statistics written by Tamir Hazan and published by MIT Press. This book was released on 2023-12-05 with total page 413 pages. Available in PDF, EPUB and Kindle. Book excerpt: A description of perturbation-based methods developed in machine learning to augment novel optimization methods with strong statistical guarantees. In nearly all machine learning, decisions must be made given current knowledge. Surprisingly, making what is believed to be the best decision is not always the best strategy, even when learning in a supervised learning setting. An emerging body of work on learning under different rules applies perturbations to decision and learning procedures. These methods provide simple and highly efficient learning rules with improved theoretical guarantees. This book describes perturbation-based methods developed in machine learning to augment novel optimization methods with strong statistical guarantees, offering readers a state-of-the-art overview. Chapters address recent modeling ideas that have arisen within the perturbations framework, including Perturb & MAP, herding, and the use of neural networks to map generic noise to distribution over highly structured data. They describe new learning procedures for perturbation models, including an improved EM algorithm and a learning algorithm that aims to match moments of model samples to moments of data. They discuss understanding the relation of perturbation models to their traditional counterparts, with one chapter showing that the perturbations viewpoint can lead to new algorithms in the traditional setting. And they consider perturbation-based regularization in neural networks, offering a more complete understanding of dropout and studying perturbations in the context of deep neural networks.

Design and Optimization for 5G Wireless Communications

Author : Haesik Kim
Publisher : John Wiley & Sons
ISBN 13 : 1119494559
Total Pages : 420 pages
Book Rating : 4.1/5 (194 download)

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Book Synopsis Design and Optimization for 5G Wireless Communications by : Haesik Kim

Download or read book Design and Optimization for 5G Wireless Communications written by Haesik Kim and published by John Wiley & Sons. This book was released on 2020-06-02 with total page 420 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book offers a technical background to the design and optimization of wireless communication systems, covering optimization algorithms for wireless and 5G communication systems design. The book introduces the design and optimization systems which target capacity, latency, and connection density; including Enhanced Mobile Broadband Communication (eMBB), Ultra-Reliable and Low Latency Communication (URLL), and Massive Machine Type Communication (mMTC). The book is organized into two distinct parts: Part I, mathematical methods and optimization algorithms for wireless communications are introduced, providing the reader with the required mathematical background. In Part II, 5G communication systems are designed and optimized using the mathematical methods and optimization algorithms.

Intelligent Computing and Optimization

Author : Pandian Vasant
Publisher : Springer Nature
ISBN 13 : 3031503279
Total Pages : 376 pages
Book Rating : 4.0/5 (315 download)

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Book Synopsis Intelligent Computing and Optimization by : Pandian Vasant

Download or read book Intelligent Computing and Optimization written by Pandian Vasant and published by Springer Nature. This book was released on 2024-01-21 with total page 376 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book of Springer Nature is another proof of Springer’s outstanding greatness on the lively interface of Holistic Computational Optimization, Green IoTs, Smart Modeling, and Deep Learning! It is a masterpiece of what our community of academics and experts can provide when an interconnected approach of joint, mutual, and meta-learning is supported by advanced operational research and experience of the World-Leader Springer Nature! The 6th edition of International Conference on Intelligent Computing and Optimization took place at G Hua Hin Resort & Mall on April 27–28, 2023, with tremendous support from the global research scholars across the planet. Objective is to celebrate “Research Novelty with Compassion and Wisdom” with researchers, scholars, experts, and investigators in Intelligent Computing and Optimization across the globe, to share knowledge, experience, and innovation—a marvelous opportunity for discourse and mutuality by novel research, invention, and creativity. This proceedings book of the 6th ICO’2023 is published by Springer Nature—Quality Label of Enlightenment.