Exploring Practical Knowledge investigates professional practices from a hermeneutic perspective. The book presents, discusses and applies notions such as practical knowledge, practical wisdom, tacit knowledge, and normativity to the professional lifeworld. These contributions focus on both specific practices and more general questions concerning theories and investigations of practice. This volume comes as the result of a cooperation of three research centres: The two Centres for Practical Knowledge in Bodø, Norway and in Södertörn, Sweden, as well as the Research Group Value-Oriented Professionalisation at the University of Applied Sciences in Utrecht, the Netherlands. It offers empirical studies of professionals as well as discussing the underlying theories, approaches and methods of exploring practical knowledge – including the limits to any articulation of these aspects of professional action. In contrast to the objectivist paradigm that otherwise dominates professional studies, each chapter presents central perspectives and possibilities drawing from humanistic and interdisciplinary research traditions. The book explores professions in a style accessible to scholars and practitioners alike. It is interesting for those studying practices within these professions and for vocational studies in education, social work, health care, police work, journalism, etc.

This book presents the fundamental concepts of organizational learning (OL) and related topics. In addition, it discusses various factors that influence the success of, and readiness to adopt, OL. In the modern competitive market, companies are looking for ways to excel by focusing more on innovation and knowledge discovery. In response, the book presents a ready-to-use tool for driving OL, called Project Action Learning (PAL). The PAL framework helps teams effectively work on, and learn from, meaningful projects. In this regard, equal emphasis is placed on achieving the project outcome and the participants’ learning objectives. Moreover, the book offers a step-by-step guidebook on how PAL-driven OL can be achieved, making it a valuable asset for educators and practitioners alike.

In this collection, Kieran Setiya explores the place of agency in ethics, arguing for a causal theory of intentional action on which it is understood through the knowledge embodied in our intentions, and against the rationalist project of deriving norms of practical reason from the nature of the will.

"Variations in computer-based training (CBT) procedures were compared in training the skills and knowledge required of a prototype map interface for the Land Warrior system. Soldiers from four Infantry courses participated, representing the chain of command within an Infantry platoon, from platoon leader to rifleman. Soldiers were first trained on codes that uniquely identified individuals and units on the map. Then soldiers learned how to use map functions such as pan, zoom, determine range, and find individuals and units. Lessons that contained a large volume of information before soldiers could apply that information and commit it to memory resulted in low scores on both the code and map exercises. Breaking: up the content into smaller chunks of information tended to be more effective. Although soldiers who learned the map on their own via an exploratory condition had the lowest map performance, exploratory learning may have potential as these soldiers spent relatively little time 'exploring'. The results demonstrate the importance of adapting to individual differences in the learning rate of soldiers. They also provide insights regarding how to design effective and efficient CBT for digital systems."--DTIC.

Drawing from the discourse of practice-oriented teacher education, this book investigates the state of pre-service teachers’ practical knowledge in mainland China, providing insights into the reform of initial teacher education programmes for teacher educators. Conducting empirical studies at a university in Beijing, involving 400 pre-service teachers, the author investigates factors influencing pre-service teachers’ practical knowledge. Five innovative methodologies, namely concept mapping, visual metaphors, video analysis, epistemic network analysis, and formative interventions are employed to make pre-service teachers’ practical knowledge visible, helping to increase our theoretical understanding of practical knowledge and proposing practical guidelines for the reorganisation of initial teacher education. While the study is grounded in mainland China, the methodological thinking and theoretical discussions can inspire international scholars and teacher educators, and therefore contribute to the global reform of teacher education.

While widely studied, the capacity of the human mind remains largely unexplored. As such, researchers are continually seeking ways to understand the brain, its function, and its impact on human behavior. Exploring Implicit Cognition: Learning, Memory, and Social Cognitive Processes explores research surrounding the ways in which an individual’s unconscious is able to influence and impact that person’s behavior without their awareness. Focusing on topics pertaining to social cognition and the unconscious process, this title is ideal for use by students, researchers, psychologists, and academicians interested in the latest insights into implicit cognition.

Train, test, run, track, store, tune, deploy, and explain provenance-aware deep learning models and pipelines at scale with reproducibility using MLflow Key Features • Focus on deep learning models and MLflow to develop practical business AI solutions at scale • Ship deep learning pipelines from experimentation to production with provenance tracking • Learn to train, run, tune and deploy deep learning pipelines with explainability and reproducibility Book Description The book starts with an overview of the deep learning (DL) life cycle and the emerging Machine Learning Ops (MLOps) field, providing a clear picture of the four pillars of deep learning: data, model, code, and explainability and the role of MLflow in these areas. From there onward, it guides you step by step in understanding the concept of MLflow experiments and usage patterns, using MLflow as a unified framework to track DL data, code and pipelines, models, parameters, and metrics at scale. You'll also tackle running DL pipelines in a distributed execution environment with reproducibility and provenance tracking, and tuning DL models through hyperparameter optimization (HPO) with Ray Tune, Optuna, and HyperBand. As you progress, you'll learn how to build a multi-step DL inference pipeline with preprocessing and postprocessing steps, deploy a DL inference pipeline for production using Ray Serve and AWS SageMaker, and finally create a DL explanation as a service (EaaS) using the popular Shapley Additive Explanations (SHAP) toolbox. By the end of this book, you'll have built the foundation and gained the hands-on experience you need to develop a DL pipeline solution from initial offline experimentation to final deployment and production, all within a reproducible and open source framework. What you will learn • Understand MLOps and deep learning life cycle development • Track deep learning models, code, data, parameters, and metrics • Build, deploy, and run deep learning model pipelines anywhere • Run hyperparameter optimization at scale to tune deep learning models • Build production-grade multi-step deep learning inference pipelines • Implement scalable deep learning explainability as a service • Deploy deep learning batch and streaming inference services • Ship practical NLP solutions from experimentation to production Who this book is for This book is for machine learning practitioners including data scientists, data engineers, ML engineers, and scientists who want to build scalable full life cycle deep learning pipelines with reproducibility and provenance tracking using MLflow. A basic understanding of data science and machine learning is necessary to grasp the concepts presented in this book.

Accelerate the adoption of machine learning by automating away the complex parts of the ML pipeline using H2O.ai Key FeaturesLearn how to train the best models with a single click using H2O AutoMLGet a simple explanation of model performance using H2O ExplainabilityEasily deploy your trained models to production using H2O MOJO and POJOBook Description With the huge amount of data being generated over the internet and the benefits that Machine Learning (ML) predictions bring to businesses, ML implementation has become a low-hanging fruit that everyone is striving for. The complex mathematics behind it, however, can be discouraging for a lot of users. This is where H2O comes in – it automates various repetitive steps, and this encapsulation helps developers focus on results rather than handling complexities. You'll begin by understanding how H2O's AutoML simplifies the implementation of ML by providing a simple, easy-to-use interface to train and use ML models. Next, you'll see how AutoML automates the entire process of training multiple models, optimizing their hyperparameters, as well as explaining their performance. As you advance, you'll find out how to leverage a Plain Old Java Object (POJO) and Model Object, Optimized (MOJO) to deploy your models to production. Throughout this book, you'll take a hands-on approach to implementation using H2O that'll enable you to set up your ML systems in no time. By the end of this H2O book, you'll be able to train and use your ML models using H2O AutoML, right from experimentation all the way to production without a single need to understand complex statistics or data science. What you will learnGet to grips with H2O AutoML and learn how to use itExplore the H2O Flow Web UIUnderstand how H2O AutoML trains the best models and automates hyperparameter optimizationFind out how H2O Explainability helps understand model performanceExplore H2O integration with scikit-learn, the Spring Framework, and Apache StormDiscover how to use H2O with Spark using H2O Sparkling WaterWho this book is for This book is for engineers and data scientists who want to quickly adopt machine learning into their products without worrying about the internal intricacies of training ML models. If you're someone who wants to incorporate machine learning into your software system but don't know where to start or don't have much expertise in the domain of ML, then you'll find this book useful. Basic knowledge of statistics and programming is beneficial. Some understanding of ML and Python will be helpful.

This book examines the relationship between cultural difference and practical knowledge and its implications for the study of humanities and the social sciences. It sketches a meta-theory of Western thought to grasp the conceptual distortions that result when a normatively structured theoretical way of understanding the world seeks to displace practical forms of understanding. The book draws on both Western thinkers such as Nietzsche, Marx, Wittgenstein and Foucault and Indian thinkers such as Gandhi, Tagore and Balagangadhara to formulate a practical epistemology that delimits theoretical knowledge by regenerating experiential knowledge that was the hallmark of Indian intellectual traditions and provides the intellectual resources for rejecting normativity. By thus preparing the ground for a radical reconceptualization of the human sciences it seeks to overcome the loss of concepts and the violence generated by the grafting of ill–understood and experience-occluding normative conceptual structures on the fabric of practical life. Finally, the author offers an alternative conceptualization of Indian sociality through the idea of a practitional matrix, which explains both why the West necessarily misunderstood or misdescribed India and how that misdescription enables us to theorize the West. Part of Critical Humanities across Cultures series, this book will be an essential read for scholars and researchers of philosophy, anthropology, sociology, religious studies, post-colonial studies, cultural studies, Indian studies and literature.

The Structures of Practical Knowledge investigates the nature of practical knowledge – why, how, when and by whom it is codified, and once codified, how this knowledge is structured. The inquiry unfolds in a series of fifteen case studies, which range in focus from early modern Italy to eighteenth century China. At the heart of each study is a shared definition of practical knowledge, that is, knowledge needed to obtain a certain outcome, whether that be an artistic or mechanical artifact, a healing practice, or a mathematical result. While the content of practical knowledge is widely variable, this study shows that all practical knowledge is formally equivalent in following a defined workflow, as reflected in a construction procedure, a recipe, or an algorithm. As explored in the volume’s fifteen contributions, there are three levels at which structures of practical knowledge may be understood and examined. At the most immediate level, there are the individual workflows that encompasses practical knowledge itself. Probing further, it is possible to examine the structure of practical knowledge as it is externalized and codified in texts, drawings, and artifacts such as models. Finally, practical knowledge is also related to social structures, which fundamentally determine its dissemination and evolution into new knowledge structures. The social structures of professionals and institutions represent the critical means by which practical knowledge takes form. These actors are the agents of codification, and by means of selection, appropriation, investment, and knowledge development, they determine the formation of new structures of practical knowledge. On a more abstract level, the creation of new knowledge structures is understood as constituting the basis for the further development of scientific knowledge. Rich in subject matter and incisive in the theory it lays out, this volume represents an important contribution to the history of science and epistemology. Individually, the fifteen case studies – encompassing the history of architecture, mining, brewing, glass production, printing, ballistics, mechanics, cartography, cosmology and astronomy – are replete with original research, and offer new insights into the history of science. Taken together, the contributions remodel historical epistemology as a whole, elucidating the underlining knowledge structures that transcend disciplinary boundaries, and that unite practitioners across time and space.