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Author: Michael Covert
Publisher: Packt Publishing Ltd
ISBN: 1785285238
Category : Computers
Languages : en
Pages : 276
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Book Description
This book is intended for software developers, system architects and analysts, big data project managers, and data scientists who wish to deploy big data solutions using the Cascading framework. You must have a basic understanding of the big data paradigm and should be familiar with Java development techniques.
Author: Michael Covert
Publisher: Packt Publishing Ltd
ISBN: 1785285238
Category : Computers
Languages : en
Pages : 276
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Book Description
This book is intended for software developers, system architects and analysts, big data project managers, and data scientists who wish to deploy big data solutions using the Cascading framework. You must have a basic understanding of the big data paradigm and should be familiar with Java development techniques.
Author: Massih-Reza Amini
Publisher: Springer Nature
ISBN: 3031263901
Category : Computers
Languages : en
Pages : 798
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Book Description
Chapters “On the Current State of Reproducibility and Reporting of Uncertainty for Aspect-Based SentimentAnalysis” and “Contextualized Graph Embeddings for Adverse Drug Event Detection” are licensed under theterms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/). For further details see license information in the chapter.
Author: Sebastian Löbner
Publisher: Springer Nature
ISBN: 3030502007
Category : Language Arts & Disciplines
Languages : en
Pages : 486
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Book Description
This open access book presents novel theoretical, empirical and experimental work exploring the nature of mental representations that support natural language production and understanding, and other manifestations of cognition. One fundamental question raised in the text is whether requisite knowledge structures can be adequately modeled by means of a uniform representational format, and if so, what exactly is its nature. Frames are a key topic covered which have had a strong impact on the exploration of knowledge representations in artificial intelligence, psychology and linguistics; cascades are a novel development in frame theory. Other key subject areas explored are: concepts and categorization, the experimental investigation of mental representation, as well as cognitive analysis in semantics. This book is of interest to students, researchers, and professionals working on cognition in the fields of linguistics, philosophy, and psychology.
Author: Zahed Ghaderi
Publisher: Walter de Gruyter GmbH & Co KG
ISBN: 3110679221
Category : Business & Economics
Languages : en
Pages : 305
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Book Description
Tourism and hospitality organizations have always been exposed to disruptions, stresses, and crises, making the management of these adversities a necessary skill. The prolonged and complex turbulence that the industry is often facing from epidemics and pandemics, climate change and extreme environmental phenomena, or political instability and economic crashes, raise questions: Do these organizations learn from the crises they experience? If so, how do they learn, and what do they do with this learning? Organizational Learning in Tourism and Hospitality Crisis Management brings together an array of expert academic analyses of the latest thinking and practice on these questions. Drawing on studies conducted around the world including Greece, Iran, Japan, Malaysia, Mexico, Spain, Turkey, UK, and USA, the contributors apply a wide range of organizational learning and knowledge management theoretical perspectives and concepts to offer new insights into crisis-induced learning in a tourism and hospitality context. The book will be an excellent resource for scholars and students as well as managers of tourism and hospitality organizations, tourism policymakers, and government officials who are involved in tourism destination management.
Author: Taeho Jo
Publisher: Springer Nature
ISBN: 3031328795
Category : Technology & Engineering
Languages : en
Pages : 433
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Book Description
This book provides a conceptual understanding of deep learning algorithms. The book consists of the four parts: foundations, deep machine learning, deep neural networks, and textual deep learning. The first part provides traditional supervised learning, traditional unsupervised learning, and ensemble learning, as the preparation for studying deep learning algorithms. The second part deals with modification of existing machine learning algorithms into deep learning algorithms. The book’s third part deals with deep neural networks, such as Multiple Perceptron, Recurrent Networks, Restricted Boltzmann Machine, and Convolutionary Neural Networks. The last part provides deep learning techniques that are specialized for text mining tasks. The book is relevant for researchers, academics, students, and professionals in machine learning.
Author: Phil Jones
Publisher: CRC Press
ISBN: 1317049276
Category : Business & Economics
Languages : en
Pages : 339
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Book Description
How can we ensure our strategy will succeed, especially in changing and uncertain times? The answer, as explained in Strategy Mapping for Learning Organizations, is to become a more responsive organization - one that captures its strategy in strategy maps, learns from that strategy and can adapt to deliver results. For anyone involved in managing strategy and performance, applying the powerful strategy mapping techniques will move your balanced scorecard from an operational tool to one of strategy and change. It will help you capture, communicate and manage your strategy more effectively. However, strategy can no longer be simply a top down, annual process. It needs to be more iterative, emergent and involving. Many agile organizations have adopted rolling plans and budgets. To bring greater agility into the wider strategy and performance management processes requires the tools and techniques described in Strategy Mapping for Learning Organizations. Phil Jones provides a detailed guide to developing, rolling out and managing with modern strategy maps and scorecards, building in agility and learning. His book incorporates the latest strategic thinking and models. It places the balanced scorecard in a wider governance context that includes the management of risk and environmental and social responsibility. Fully illustrated with examples from many different organizations, this book will help you deliver your strategy better.
Author: Rajender Kumar
Publisher: Jamba Academy
ISBN: 1960833006
Category : Computers
Languages : en
Pages : 504
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Book Description
Are you ready to dive into the world of Python machine learning? Look no further! "Python Machine Learning: A Beginner's Guide to Scikit-Learn" is the perfect guide for you. Written by experienced data scientist, Rajender Kumar, this book takes you on a journey through the basics of machine learning and the powerful Scikit-learn library. Key Features: Detailed introduction to the fundamentals of machine learning and the Scikit-Learn library. Comprehensive coverage of essential concepts such as data preprocessing, model selection, evaluation, and optimization. Hands-on experience with real-world datasets and practical projects that will help you develop the skills you need to succeed in machine learning. Easy-to-follow explanations and step-by-step examples that make it easy for beginners to get started and advanced users to take their skills to the next level. See how machine learning is being used to solve problems in industries such as healthcare, finance and more. This book is perfect for beginners who are new to machine learning and want to learn Scikit-Learn from scratch. It is also ideal for intermediate and advanced users who want to expand their knowledge and build more complex models. Outcome: Unlock the earning potential of up to $300k in job after reading the book. Boosting your resume. Opening doors to new opportunities. What other people says: Don't just take our word for it - see what other readers have said: "I was able to understand machine learning concepts and implement them easily with the help of this book." "Rajender Kumar's writing style made the complex concepts easy to understand." "I highly recommend this book to anyone looking to learn machine learning with Python." Don't miss out on this opportunity to master the art of Python machine learning with "Python Machine Learning: A Beginner's Guide to Scikit-Learn". Get your copy today and start building your own intelligent systems! WHO THIS BOOK IS FOR? "Python Machine Learning: A Beginner's Guide to Scikit-Learn" is intended for a wide range of readers, including: Individuals who are new to the field of machine learning and want to gain a solid understanding of the basics and how to apply them using the popular scikit-learn library in Python. Data scientists, statisticians, and analysts who are familiar with machine learning concepts but want to learn how to implement them using Python and scikit-learn. Developers and engineers who want to add machine learning to their skill set and build intelligent applications using Python. Students and researchers who are studying machine learning and want to learn how to apply it using a widely used and accessible library like scikit-learn. Table of Contents Introduction to Machine Learning Python: A Beginner's Overview Data Preparation Supervised Learning Unsupervised Learning Deep Learning Model Selection and Evaluation The Power of Combining: Ensemble Learning Methods Real-World Applications of Machine Learning Future Directions in Python Machine Learning Additional Resources Tools and Frameworks Datasets Career Resources Glossary
Author: Fangxing Li
Publisher: Springer Nature
ISBN: 3031453573
Category : Technology & Engineering
Languages : en
Pages : 111
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Book Description
This book provides readers with an in-depth review of deep learning-based techniques and discusses how they can benefit power system applications. Representative case studies of deep learning techniques in power systems are investigated and discussed, including convolutional neural networks (CNN) for power system security screening and cascading failure assessment, deep neural networks (DNN) for demand response management, and deep reinforcement learning (deep RL) for heating, ventilation, and air conditioning (HVAC) control. Deep Learning for Power System Applications: Case Studies Linking Artificial Intelligence and Power Systems is an ideal resource for professors, students, and industrial and government researchers in power systems, as well as practicing engineers and AI researchers. Provides a history of AI in power grid operation and planning; Introduces deep learning algorithms and applications in power systems; Includes several representative case studies.
Author: Alan Beaulieu
Publisher: O'Reilly Media
ISBN: 059655107X
Category : Computers
Languages : en
Pages : 338
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Book Description
Updated for the latest database management systems -- including MySQL 6.0, Oracle 11g, and Microsoft's SQL Server 2008 -- this introductory guide will get you up and running with SQL quickly. Whether you need to write database applications, perform administrative tasks, or generate reports, Learning SQL, Second Edition, will help you easily master all the SQL fundamentals. Each chapter presents a self-contained lesson on a key SQL concept or technique, with numerous illustrations and annotated examples. Exercises at the end of each chapter let you practice the skills you learn. With this book, you will: Move quickly through SQL basics and learn several advanced features Use SQL data statements to generate, manipulate, and retrieve data Create database objects, such as tables, indexes, and constraints, using SQL schema statements Learn how data sets interact with queries, and understand the importance of subqueries Convert and manipulate data with SQL's built-in functions, and use conditional logic in data statements Knowledge of SQL is a must for interacting with data. With Learning SQL, you'll quickly learn how to put the power and flexibility of this language to work.
Author: Yan Jia
Publisher: Springer Nature
ISBN: 3030715906
Category : Computers
Languages : en
Pages : 255
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Book Description
Knowledge representation is an important task in understanding how humans think and learn. Although many representation models or cognitive models have been proposed, such as expert systems or knowledge graphs, they cannot represent procedural knowledge, i.e., dynamic knowledge, in an efficient way. This book introduces a new knowledge representation model called MDATA (Multi-dimensional Data Association and inTelligent Analysis). By modifying the representation of entities and relations in knowledge graphs, dynamic knowledge can be efficiently described with temporal and spatial characteristics. The MDATA model can be regarded as a high-level temporal and spatial knowledge graph model, which has strong capabilities for knowledge representation. This book introduces some key technologies in the MDATA model, such as entity recognition, relation extraction, entity alignment, and knowledge reasoning with spatiotemporal factors. The MDATA model can be applied in many critical applications and this book introduces some typical examples, such as network attack detection, social network analysis, and epidemic assessment. The MDATA model should be of interest to readers from many research fields, such as database, cyberspace security, and social network, as the need for the knowledge representation arises naturally in many practical scenarios.
