Author: Ajay Pangarkar
Publisher: Kogan Page Publishers
ISBN: 1398615374
Category : Business & Economics
Languages : en
Pages : 313
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Book Description
How do I know if Learning and Development (L&D) initiatives are making a difference to employee and business performance? What should I measure and how do I take a data-driven approach? Learning Metrics is a practical book for L&D professionals which answers these questions and provides guidance for measuring and communicating the impact of L&D activity in the business. It covers how to measure participant engagement with learning, learning retention and changes in employee performance post-learning. There is also guidance on how to measure on-the-job behaviour change as a result of learning, as well as metrics for measuring the organizational impact of learning. This book also has essential guidance on how to use learning analytics to assess the return on investment (ROI) of learning and how to use these results to adopt a data-driven approach and map learning metrics to organizational KPIs. Learning Metrics also includes guidance on how to communicate the impact of the company's learning and development activity to all business stakeholders. Full of practical guidance, examples, advice and case studies from organizations including Pfizer, Goldman Sachs, HSBC, Merck and Hewlett Packard, this is essential reading for all L&D professionals needing to measure the impact of L&D activity in the business but not sure where to start.
Author: Haibo He
Publisher: John Wiley & Sons
ISBN: 1118646339
Category : Technology & Engineering
Languages : en
Pages : 222
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Book Description
The first book of its kind to review the current status and future direction of the exciting new branch of machine learning/data mining called imbalanced learning Imbalanced learning focuses on how an intelligent system can learn when it is provided with imbalanced data. Solving imbalanced learning problems is critical in numerous data-intensive networked systems, including surveillance, security, Internet, finance, biomedical, defense, and more. Due to the inherent complex characteristics of imbalanced data sets, learning from such data requires new understandings, principles, algorithms, and tools to transform vast amounts of raw data efficiently into information and knowledge representation. The first comprehensive look at this new branch of machine learning, this book offers a critical review of the problem of imbalanced learning, covering the state of the art in techniques, principles, and real-world applications. Featuring contributions from experts in both academia and industry, Imbalanced Learning: Foundations, Algorithms, and Applications provides chapter coverage on: Foundations of Imbalanced Learning Imbalanced Datasets: From Sampling to Classifiers Ensemble Methods for Class Imbalance Learning Class Imbalance Learning Methods for Support Vector Machines Class Imbalance and Active Learning Nonstationary Stream Data Learning with Imbalanced Class Distribution Assessment Metrics for Imbalanced Learning Imbalanced Learning: Foundations, Algorithms, and Applications will help scientists and engineers learn how to tackle the problem of learning from imbalanced datasets, and gain insight into current developments in the field as well as future research directions.
Author: Umberto Michelucci
Publisher: Springer Nature
ISBN: 3031564316
Category : Machine learning
Languages : en
Pages : 259
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Book Description
This book is for individuals with a scientific background who aspire to apply machine learning within various natural science disciplinessuch as physics, chemistry, biology, medicine, psychology and many more. It elucidates core mathematical concepts in an accessible and straightforward manner, maintaining rigorous mathematical integrity. For readers more versed in mathematics, the book includes advanced sections that are not prerequisites for the initial reading. It ensures concepts are clearly defined and theorems are proven where it's pertinent. Machine learning transcends the mere implementation and training of algorithms; it encompasses the broader challenges of constructing robust datasets, model validation, addressing imbalanced datasets, and fine-tuning hyperparameters. These topics are thoroughly examined within the text, along with the theoretical foundations underlying these methods. Rather than concentrating on particular algorithms this book focuses on the comprehensive concepts and theories essential for their application. It stands as an indispensable resource for any scientist keen on integrating machine learning effectively into their research. Numerous texts delve into the technical execution of machine learning algorithms, often overlooking the foundational concepts vital for fully grasping these methods. This leads to a gap in using these algorithms effectively across diverse disciplines. For instance, a firm grasp of calculus is imperative to comprehend the training processes of algorithms and neural networks, while linear algebra is essential for the application and efficient training of various algorithms, including neural networks. Absent a solid mathematical base, machine learning applications may be, at best, cursory, or at worst, fundamentally flawed. This book lays the foundation for a comprehensive understanding of machine learning algorithms and approaches.
Author: Lan Zou
Publisher: Elsevier
ISBN: 0323903703
Category : Computers
Languages : en
Pages : 404
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Book Description
Deep neural networks (DNNs) with their dense and complex algorithms provide real possibilities for Artificial General Intelligence (AGI). Meta-learning with DNNs brings AGI much closer: artificial agents solving intelligent tasks that human beings can achieve, even transcending what they can achieve. Meta-Learning: Theory, Algorithms and Applications shows how meta-learning in combination with DNNs advances towards AGI. Meta-Learning: Theory, Algorithms and Applications explains the fundamentals of meta-learning by providing answers to these questions: What is meta-learning?; why do we need meta-learning?; how are self-improved meta-learning mechanisms heading for AGI ?; how can we use meta-learning in our approach to specific scenarios? The book presents the background of seven mainstream paradigms: meta-learning, few-shot learning, deep learning, transfer learning, machine learning, probabilistic modeling, and Bayesian inference. It then explains important state-of-the-art mechanisms and their variants for meta-learning, including memory-augmented neural networks, meta-networks, convolutional Siamese neural networks, matching networks, prototypical networks, relation networks, LSTM meta-learning, model-agnostic meta-learning, and the Reptile algorithm. The book takes a deep dive into nearly 200 state-of-the-art meta-learning algorithms from top tier conferences (e.g. NeurIPS, ICML, CVPR, ACL, ICLR, KDD). It systematically investigates 39 categories of tasks from 11 real-world application fields: Computer Vision, Natural Language Processing, Meta-Reinforcement Learning, Healthcare, Finance and Economy, Construction Materials, Graphic Neural Networks, Program Synthesis, Smart City, Recommended Systems, and Climate Science. Each application field concludes by looking at future trends or by giving a summary of available resources. Meta-Learning: Theory, Algorithms and Applications is a great resource to understand the principles of meta-learning and to learn state-of-the-art meta-learning algorithms, giving the student, researcher and industry professional the ability to apply meta-learning for various novel applications. A comprehensive overview of state-of-the-art meta-learning techniques and methods associated with deep neural networks together with a broad range of application areas Coverage of nearly 200 state-of-the-art meta-learning algorithms, which are promoted by premier global AI conferences and journals, and 300 to 450 pieces of key research Systematic and detailed exploration of the most crucial state-of-the-art meta-learning algorithm mechanisms: model-based, metric-based, and optimization-based Provides solutions to the limitations of using deep learning and/or machine learning methods, particularly with small sample sizes and unlabeled data Gives an understanding of how meta-learning acts as a stepping stone to Artificial General Intelligence in 39 categories of tasks from 11 real-world application fields
Author: Brian Tjemkes
Publisher: Taylor & Francis
ISBN: 100089200X
Category : Business & Economics
Languages : en
Pages : 587
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Book Description
Strategic Alliance Management presents an academically grounded alliance development framework, detailing eight stages of alliance development with consideration for specific management challenges. For each stage, readers are presented with theoretical insights, evidence-based managerial guidelines and a business case illustration. Other chapters consider alliance attributes, alliance competences, and alliance challenges, and cover topics such as innovation, co-branding, co-opetition, business ecosystems, alliance professionals, alliance capabilities, societal alliances and a tension-based alliance mindset. This fully revised 3rd edition leverages the book’s strengths in marrying theory with practical insight. All the chapters have been updated to reflect the current academic literature, whilst new international case studies are incorporated throughout. Two new chapters feature in this edition, considering the importance of the mindset required to successfully navigate alliance arrangements, and emerging alliance practices, exploring how new technologies, sustainability and the external environment have disrupted alliance management. In-chapter text boxes discussing emerging themes provide opportunity for discussion and analysis. The textbook remains highly valuable core and recommended reading for postgraduate students of Strategic Management and Corporate Strategy, MBA and Executive MBA, as well as reflective practitioners in the field. Online resources include chapter-by-chapter lecture slides, two long case studies and short interviews with alliance executives.
Author: Don Morrison
Publisher: John Wiley & Sons
ISBN: 047086432X
Category : Business & Economics
Languages : en
Pages : 426
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Book Description
As more than 90% of spending on the Internet comes from brick and mortar companies it is these operations that will form the client base for e-learning. This book shows those companies how to get e-learning implementation right first time. Don Morisson explores and explains the whole implementation continuum - strategy, vendor selection, technology, implementation, culture change, content development and delivery. Most importantly he stresses that the success or failure of an e-learning initiative is directly related to the underlying strategic thinking. Written for a more mature, second generation e-learning market the book provides a practitioner's handbook to both guide the novice and inform the veteran. * Focuses on the reader's needs * Focuses on the strategic issues of e-learning * Informed by key business drivers * Supported and endorsed by PWC Readership: Senior managers including CEOs, CIOs, CLOs, HR Directors, middle management responsible for implementing and/or delivering e-learning, consultants
Author: Elmer P. Dadios
Publisher: BoD – Books on Demand
ISBN: 1839691735
Category : Technology & Engineering
Languages : en
Pages : 142
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Book Description
Advances in automation and control today cover many areas of technology where human input is minimized. This book discusses numerous types and applications of automation and control. Chapters address topics such as building information modeling (BIM)–based automated code compliance checking (ACCC), control algorithms useful for military operations and video games, rescue competitions using unmanned aerial-ground robots, and stochastic control systems.
Author: Kumar Abhishek
Publisher: Packt Publishing Ltd
ISBN: 1801070881
Category : Computers
Languages : en
Pages : 344
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Book Description
Take your machine learning expertise to the next level with this essential guide, utilizing libraries like imbalanced-learn, PyTorch, scikit-learn, pandas, and NumPy to maximize model performance and tackle imbalanced data Key Features Understand how to use modern machine learning frameworks with detailed explanations, illustrations, and code samples Learn cutting-edge deep learning techniques to overcome data imbalance Explore different methods for dealing with skewed data in ML and DL applications Purchase of the print or Kindle book includes a free eBook in the PDF format Book DescriptionAs machine learning practitioners, we often encounter imbalanced datasets in which one class has considerably fewer instances than the other. Many machine learning algorithms assume an equilibrium between majority and minority classes, leading to suboptimal performance on imbalanced data. This comprehensive guide helps you address this class imbalance to significantly improve model performance. Machine Learning for Imbalanced Data begins by introducing you to the challenges posed by imbalanced datasets and the importance of addressing these issues. It then guides you through techniques that enhance the performance of classical machine learning models when using imbalanced data, including various sampling and cost-sensitive learning methods. As you progress, you’ll delve into similar and more advanced techniques for deep learning models, employing PyTorch as the primary framework. Throughout the book, hands-on examples will provide working and reproducible code that’ll demonstrate the practical implementation of each technique. By the end of this book, you’ll be adept at identifying and addressing class imbalances and confidently applying various techniques, including sampling, cost-sensitive techniques, and threshold adjustment, while using traditional machine learning or deep learning models.What you will learn Use imbalanced data in your machine learning models effectively Explore the metrics used when classes are imbalanced Understand how and when to apply various sampling methods such as over-sampling and under-sampling Apply data-based, algorithm-based, and hybrid approaches to deal with class imbalance Combine and choose from various options for data balancing while avoiding common pitfalls Understand the concepts of model calibration and threshold adjustment in the context of dealing with imbalanced datasets Who this book is for This book is for machine learning practitioners who want to effectively address the challenges of imbalanced datasets in their projects. Data scientists, machine learning engineers/scientists, research scientists/engineers, and data scientists/engineers will find this book helpful. Though complete beginners are welcome to read this book, some familiarity with core machine learning concepts will help readers maximize the benefits and insights gained from this comprehensive resource.
Author: Guillaume Coqueret
Publisher: CRC Press
ISBN: 1000912809
Category : Mathematics
Languages : en
Pages : 358
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Book Description
a detailed presentation of the key machine learning tools use in finance a large scale coding tutorial with easily reproducible examples realistic applications on a large publicly available dataset all the key ingredients to perform a full portfolio backtest
Author: Keshav Kaushik
Publisher: CRC Press
ISBN: 1000820130
Category : Computers
Languages : en
Pages : 241
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Book Description
The quantity, diversity, and sophistication of Internet of Things (IoT) items are rapidly increasing, posing significant issues but also innovative solutions for forensic science. Such systems are becoming increasingly common in public locations, businesses, universities, residences, and other shared offices, producing enormous amounts of data at rapid speeds in a variety of forms. IoT devices can be used as suspects, digital witnesses, or instruments of crime and cyberattacks, posing new investigation problems, forensic issues, security threats, legal concerns, privacy concerns, and ethical dilemmas. A cyberattack on IoT devices might target the device itself or associated systems, particularly vital infrastructure. This book discusses the advancements in IoT and Cyber Physical Systems (CPS) forensics. The first objective is to learn and understand the fundamentals of IoT forensics. This objective will answer the question of why and how IoT has evolved as one of the most promising and widely accepted technologies across the globe and has many widely accepted applications. The second objective is to learn how to use CPS to address many computational problems. CPS forensics is a promising domain, and there are various advancements in this field. This book is structured so that the topics of discussion are relevant to each reader’s particular areas of interest. The book’s goal is to help each reader to see the relevance of IoT and CPS forensics to his or her career or interests. This book not only presents numerous case studies from a global perspective, but it also compiles a large amount of literature and research from a database. As a result, this book effectively demonstrates the concerns, difficulties, and trends surrounding the topic while also encouraging readers to think globally. The main goal of this project is to encourage both researchers and practitioners to share and exchange their experiences and recent studies between academia and industry.
