Optimizing Data To Learning To Action

Download Optimizing Data To Learning To Action full books in PDF, epub, and Kindle. Read online Optimizing Data To Learning To Action ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!

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)

DOWNLOAD NOW!

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

Optimizing Data-to-learning-to-action

Author : Steven D. Flinn
Publisher :
ISBN 13 : 9781484235324
Total Pages : pages
Book Rating : 4.2/5 (353 download)

DOWNLOAD NOW!

Book Synopsis Optimizing Data-to-learning-to-action by : Steven D. Flinn

Download or read book Optimizing Data-to-learning-to-action written by Steven D. Flinn and published by . This book was released on 2018 with total page 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.

Machine Learning, Optimization, and Big Data

Author : Panos M. Pardalos
Publisher : Springer
ISBN 13 : 3319514695
Total Pages : 456 pages
Book Rating : 4.3/5 (195 download)

DOWNLOAD NOW!

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 456 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.

Machine Learning, Optimization, and Data Science

Author : Giuseppe Nicosia
Publisher : Springer Nature
ISBN 13 : 3030375994
Total Pages : 798 pages
Book Rating : 4.0/5 (33 download)

DOWNLOAD NOW!

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 2020-01-03 with total page 798 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the post-conference proceedings of the 5th International Conference on Machine Learning, Optimization, and Data Science, LOD 2019, held in Siena, Italy, in September 2019. The 54 full papers presented were carefully reviewed and selected from 158 submissions. The papers cover topics in the field of machine learning, artificial intelligence, reinforcement learning, computational optimization and data science presenting a substantial array of ideas, technologies, algorithms, methods and applications.

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)

DOWNLOAD NOW!

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 : Michael J. Marquardt
Publisher : Hachette UK
ISBN 13 : 1529353424
Total Pages : 304 pages
Book Rating : 4.5/5 (293 download)

DOWNLOAD NOW!

Book Synopsis Optimizing the Power of Action Learning by : Michael J. Marquardt

Download or read book Optimizing the Power of Action Learning written by Michael J. Marquardt and published by Hachette UK. This book was released on 2018-06-14 with total page 304 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Third Edition of the field-defining book Originated by Reg Revans in the 1940s, the Action Learning Model was refined and then reintroduced by lead author Michael Marquardt to organizations globally as a powerful tool for improving organizational performance. Today, Marquardt is widely considered to be the modern "father" of the Action Learning approach. For this new edition, Marquardt has teamed up with three Action Learning experts from Asia and the UK who bring a broader global approach to what has become THE seminal book in the field. Each chapter has been updated for alignment with today's practice and implementation of Action Learning in organizations, including fresh information on virtual Action Learning, guidance on implementing Action Learning and becoming a Certified Action Learning Coach, and many new case studies.

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)

DOWNLOAD NOW!

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.

Artificial Intelligence for Business Optimization

Author : Bhuvan Unhelkar
Publisher : CRC Press
ISBN 13 : 1000409430
Total Pages : 324 pages
Book Rating : 4.0/5 (4 download)

DOWNLOAD NOW!

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 324 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.

Machine Learning, Optimization, and Big Data

Author : Panos M. Pardalos
Publisher : Springer
ISBN 13 : 9783319514680
Total Pages : 0 pages
Book Rating : 4.5/5 (146 download)

DOWNLOAD NOW!

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 2016-12-25 with total page 0 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.

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)

DOWNLOAD NOW!

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 in Action

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

DOWNLOAD NOW!

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

Patterns, Predictions, and Actions: Foundations of Machine Learning

Author : Moritz Hardt
Publisher : Princeton University Press
ISBN 13 : 0691233721
Total Pages : 321 pages
Book Rating : 4.6/5 (912 download)

DOWNLOAD NOW!

Book Synopsis Patterns, Predictions, and Actions: Foundations of Machine Learning by : Moritz Hardt

Download or read book Patterns, Predictions, and Actions: Foundations of Machine Learning written by Moritz Hardt and published by Princeton University Press. This book was released on 2022-08-23 with total page 321 pages. Available in PDF, EPUB and Kindle. Book excerpt: An authoritative, up-to-date graduate textbook on machine learning that highlights its historical context and societal impacts Patterns, Predictions, and Actions introduces graduate students to the essentials of machine learning while offering invaluable perspective on its history and social implications. Beginning with the foundations of decision making, Moritz Hardt and Benjamin Recht explain how representation, optimization, and generalization are the constituents of supervised learning. They go on to provide self-contained discussions of causality, the practice of causal inference, sequential decision making, and reinforcement learning, equipping readers with the concepts and tools they need to assess the consequences that may arise from acting on statistical decisions. Provides a modern introduction to machine learning, showing how data patterns support predictions and consequential actions Pays special attention to societal impacts and fairness in decision making Traces the development of machine learning from its origins to today Features a novel chapter on machine learning benchmarks and datasets Invites readers from all backgrounds, requiring some experience with probability, calculus, and linear algebra An essential textbook for students and a guide for researchers

Automated Machine Learning in Action

Author : Qingquan Song
Publisher : Simon and Schuster
ISBN 13 : 1617298050
Total Pages : 334 pages
Book Rating : 4.6/5 (172 download)

DOWNLOAD NOW!

Book Synopsis Automated Machine Learning in Action by : Qingquan Song

Download or read book Automated Machine Learning in Action written by Qingquan Song and published by Simon and Schuster. This book was released on 2022-06-07 with total page 334 pages. Available in PDF, EPUB and Kindle. Book excerpt: Automated Machine Learning in Action reveals how you can automate the burdensome elements of designing and tuning your machine learning systems. --

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)

DOWNLOAD NOW!

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.

Machine Learning, Optimization, and Big Data

Author : Giuseppe Nicosia
Publisher : Springer
ISBN 13 : 3319729268
Total Pages : 621 pages
Book Rating : 4.3/5 (197 download)

DOWNLOAD NOW!

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

Download or read book Machine Learning, Optimization, and Big Data written by Giuseppe Nicosia and published by Springer. This book was released on 2017-12-19 with total page 621 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the post-conference proceedings of the Third International Workshop on Machine Learning, Optimization, and Big Data, MOD 2017, held in Volterra, Italy, in September 2017. The 50 full papers presented were carefully reviewed and selected from 126 submissions. The papers cover topics in the field of machine learning, artificial intelligence, computational optimization and data science presenting a substantial array of ideas, technologies, algorithms, methods and applications.

Intelligent Computing & Optimization

Author : Pandian Vasant
Publisher : Springer Nature
ISBN 13 : 3031199588
Total Pages : 1215 pages
Book Rating : 4.0/5 (311 download)

DOWNLOAD NOW!

Book Synopsis Intelligent Computing & Optimization by : Pandian Vasant

Download or read book Intelligent Computing & Optimization written by Pandian Vasant and published by Springer Nature. This book was released on 2022-10-20 with total page 1215 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book of Springer Nature is another proof of Springer’s outstanding and greatness on the lively interface of Smart Computational Optimization, Green ICT, Smart Intelligence and Machine Learning! It is a Master Piece of what our community of academics and experts can provide when an Interconnected Approach of Joint, Mutual and Meta Learning is supported by Modern Operational Research and Experience of the World-Leader Springer Nature! The 5th edition of International Conference on Intelligent Computing and Optimization took place at October 27–28, 2022, via Zoom. Objective was to celebrate “Creativity with Compassion and Wisdom” with researchers, scholars, experts and investigators in Intelligent Computing and Optimization across the planet, to share knowledge, experience, innovation—a marvelous opportunity for discourse and mutuality by novel research, invention and creativity. This proceedings book of ICO’2022 is published by Springer Nature—Quality Label of wonderful.

Learning and Intelligent Optimization

Author : Roberto Battiti
Publisher : Springer
ISBN 13 : 3030053482
Total Pages : 487 pages
Book Rating : 4.0/5 (3 download)

DOWNLOAD NOW!

Book Synopsis Learning and Intelligent Optimization by : Roberto Battiti

Download or read book Learning and Intelligent Optimization written by Roberto Battiti and published by Springer. This book was released on 2018-12-31 with total page 487 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the thoroughly refereed post-conference proceedings of the 12th International Conference on Learning and Intelligent Optimization, LION 12, held in Kalamata, Greece, in June 2018. The 28 full papers and 12 short papers presented have been carefully reviewed and selected from 62 submissions. The papers explore the advanced research developments in such interconnected fields as mathematical programming, global optimization, machine learning, and artificial intelligence. Special focus is given to advanced ideas, technologies, methods, and applications in optimization and machine learning.