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Author: Trevor Grant Publisher: "O'Reilly Media, Inc." ISBN: 1492050075 Category : Computers Languages : en Pages : 264
Book Description
If you're training a machine learning model but aren't sure how to put it into production, this book will get you there. Kubeflow provides a collection of cloud native tools for different stages of a model's lifecycle, from data exploration, feature preparation, and model training to model serving. This guide helps data scientists build production-grade machine learning implementations with Kubeflow and shows data engineers how to make models scalable and reliable. Using examples throughout the book, authors Holden Karau, Trevor Grant, Ilan Filonenko, Richard Liu, and Boris Lublinsky explain how to use Kubeflow to train and serve your machine learning models on top of Kubernetes in the cloud or in a development environment on-premises. Understand Kubeflow's design, core components, and the problems it solves Understand the differences between Kubeflow on different cluster types Train models using Kubeflow with popular tools including Scikit-learn, TensorFlow, and Apache Spark Keep your model up to date with Kubeflow Pipelines Understand how to capture model training metadata Explore how to extend Kubeflow with additional open source tools Use hyperparameter tuning for training Learn how to serve your model in production
Author: Trevor Grant Publisher: "O'Reilly Media, Inc." ISBN: 1492050075 Category : Computers Languages : en Pages : 264
Book Description
If you're training a machine learning model but aren't sure how to put it into production, this book will get you there. Kubeflow provides a collection of cloud native tools for different stages of a model's lifecycle, from data exploration, feature preparation, and model training to model serving. This guide helps data scientists build production-grade machine learning implementations with Kubeflow and shows data engineers how to make models scalable and reliable. Using examples throughout the book, authors Holden Karau, Trevor Grant, Ilan Filonenko, Richard Liu, and Boris Lublinsky explain how to use Kubeflow to train and serve your machine learning models on top of Kubernetes in the cloud or in a development environment on-premises. Understand Kubeflow's design, core components, and the problems it solves Understand the differences between Kubeflow on different cluster types Train models using Kubeflow with popular tools including Scikit-learn, TensorFlow, and Apache Spark Keep your model up to date with Kubeflow Pipelines Understand how to capture model training metadata Explore how to extend Kubeflow with additional open source tools Use hyperparameter tuning for training Learn how to serve your model in production
Author: Henrik Brink Publisher: Simon and Schuster ISBN: 1638357005 Category : Computers Languages : en Pages : 380
Book Description
Summary Real-World Machine Learning is a practical guide designed to teach working developers the art of ML project execution. Without overdosing you on academic theory and complex mathematics, it introduces the day-to-day practice of machine learning, preparing you to successfully build and deploy powerful ML systems. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Machine learning systems help you find valuable insights and patterns in data, which you'd never recognize with traditional methods. In the real world, ML techniques give you a way to identify trends, forecast behavior, and make fact-based recommendations. It's a hot and growing field, and up-to-speed ML developers are in demand. About the Book Real-World Machine Learning will teach you the concepts and techniques you need to be a successful machine learning practitioner without overdosing you on abstract theory and complex mathematics. By working through immediately relevant examples in Python, you'll build skills in data acquisition and modeling, classification, and regression. You'll also explore the most important tasks like model validation, optimization, scalability, and real-time streaming. When you're done, you'll be ready to successfully build, deploy, and maintain your own powerful ML systems. What's Inside Predicting future behavior Performance evaluation and optimization Analyzing sentiment and making recommendations About the Reader No prior machine learning experience assumed. Readers should know Python. About the Authors Henrik Brink, Joseph Richards and Mark Fetherolf are experienced data scientists engaged in the daily practice of machine learning. Table of Contents PART 1: THE MACHINE-LEARNING WORKFLOW What is machine learning? Real-world data Modeling and prediction Model evaluation and optimization Basic feature engineering PART 2: PRACTICAL APPLICATION Example: NYC taxi data Advanced feature engineering Advanced NLP example: movie review sentiment Scaling machine-learning workflows Example: digital display advertising
Author: Robert Munro Publisher: Simon and Schuster ISBN: 1617296740 Category : Computers Languages : en Pages : 422
Book Description
Machine learning applications perform better with human feedback. Keeping the right people in the loop improves the accuracy of models, reduces errors in data, lowers costs, and helps you ship models faster. Human-in-the-loop machine learning lays out methods for humans and machines to work together effectively. You'll find best practices on selecting sample data for human feedback, quality control for human annotations, and designing annotation interfaces. You'll learn to dreate training data for labeling, object detection, and semantic segmentation, sequence labeling, and more. The book starts with the basics and progresses to advanced techniques like transfer learning and self-supervision within annotation workflows.
Author: Dawn A. Morley Publisher: Springer Nature ISBN: 3030469514 Category : Education Languages : en Pages : 425
Book Description
This open access book critiques real world learning across both the curriculum and extracurricular activities. Drawing on disciplines as diverse as business, health, fashion, sociology and geography, the editors and authors employ a cross-disciplinary approach to examine how this concept is being applied in higher education. Divided into three parts, the authors and contributors analyse broader applications of real world learning, student experience of practicing in a real world setting, and how learning strategies can be employed to engage students in real world learning. The editors and contributors provide up-to-date, cross-disciplinary and international insights into how real world learning could be integrated into the higher education curriculum to support effective, relevant and life-long learning for 21st century students.
Author: Laura Saunders Publisher: ISBN: 9781946011091 Category : Academic libraries Languages : en Pages : 389
Book Description
"This open access textbook offers a comprehensive introduction to instruction in all types of library and information settings. Designed for students in library instruction courses, the text is also a resource for new and experienced professionals seeking best practices and selected resources to support their instructional practice. Organized around the backward design approach and written by LIS faculty members with expertise in teaching and learning, this book offers clear guidance on writing learning outcomes, designing assessments, and choosing and implementing instructional strategies, framed by clear and accessible explanations of learning theories. The text takes a critical approach to pedagogy and emphasizes inclusive and accessible instruction. Using a theory into practice approach that will move students from learning to praxis, each chapter includes practical examples, activities, and templates to aid readers in developing their own practice and materials."--Publisher's description.
Author: National Research Council Publisher: National Academies Press ISBN: 0309089506 Category : Education Languages : en Pages : 265
Book Description
How Students Learn: Science in the Classroom builds on the discoveries detailed in the best-selling How People Learn. Now these findings are presented in a way that teachers can use immediately, to revitalize their work in the classroom for even greater effectiveness. Organized for utility, the book explores how the principles of learning can be applied in science at three levels: elementary, middle, and high school. Leading educators explain in detail how they developed successful curricula and teaching approaches, presenting strategies that serve as models for curriculum development and classroom instruction. Their recounting of personal teaching experiences lends strength and warmth to this volume. This book discusses how to build straightforward science experiments into true understanding of scientific principles. It also features illustrated suggestions for classroom activities.
Author: Norbert M. Seel Publisher: Springer Science & Business Media ISBN: 1441914277 Category : Education Languages : en Pages : 3643
Book Description
Over the past century, educational psychologists and researchers have posited many theories to explain how individuals learn, i.e. how they acquire, organize and deploy knowledge and skills. The 20th century can be considered the century of psychology on learning and related fields of interest (such as motivation, cognition, metacognition etc.) and it is fascinating to see the various mainstreams of learning, remembered and forgotten over the 20th century and note that basic assumptions of early theories survived several paradigm shifts of psychology and epistemology. Beyond folk psychology and its naïve theories of learning, psychological learning theories can be grouped into some basic categories, such as behaviorist learning theories, connectionist learning theories, cognitive learning theories, constructivist learning theories, and social learning theories. Learning theories are not limited to psychology and related fields of interest but rather we can find the topic of learning in various disciplines, such as philosophy and epistemology, education, information science, biology, and – as a result of the emergence of computer technologies – especially also in the field of computer sciences and artificial intelligence. As a consequence, machine learning struck a chord in the 1980s and became an important field of the learning sciences in general. As the learning sciences became more specialized and complex, the various fields of interest were widely spread and separated from each other; as a consequence, even presently, there is no comprehensive overview of the sciences of learning or the central theoretical concepts and vocabulary on which researchers rely. The Encyclopedia of the Sciences of Learning provides an up-to-date, broad and authoritative coverage of the specific terms mostly used in the sciences of learning and its related fields, including relevant areas of instruction, pedagogy, cognitive sciences, and especially machine learning and knowledge engineering. This modern compendium will be an indispensable source of information for scientists, educators, engineers, and technical staff active in all fields of learning. More specifically, the Encyclopedia provides fast access to the most relevant theoretical terms provides up-to-date, broad and authoritative coverage of the most important theories within the various fields of the learning sciences and adjacent sciences and communication technologies; supplies clear and precise explanations of the theoretical terms, cross-references to related entries and up-to-date references to important research and publications. The Encyclopedia also contains biographical entries of individuals who have substantially contributed to the sciences of learning; the entries are written by a distinguished panel of researchers in the various fields of the learning sciences.
Author: William H. Robertson Publisher: Corwin Press ISBN: 145225656X Category : Education Languages : en Pages : 145
Book Description
This book provides an approach to physical science instruction in a way that is interesting and engaging to students featuring author-created action sports videos and classroom activities focused on physical science concepts.
Author: Charles C. Bonwell Publisher: ISBN: Category : Languages : en Pages : 136
Book Description
This monograph examines the nature of active learning at the higher education level, the empirical research on its use, the common obstacles and barriers that give rise to faculty resistance, and how faculty and staff can implement active learning techniques. A preliminary section defines active learning and looks at the current climate surrounding the concept. A second section, entitled "The Modified Lecture" offers ways that teachers can incorporate active learning into their most frequently used format: the lecture. The following section on classroom discussion explains the conditions and techniques needed for the most useful type of exchange. Other ways to promote active learning are also described including: visual learning, writing in class, problem solving, computer-based instruction, cooperative learning, debates, drama, role playing, simulations, games, and peer teaching. A section on obstacles to implementing active learning techniques leads naturally to the final section, "Conclusions and Recommendations," which outlines the roles that each group within the university can play in order to encourage the implementation of active learning strategies. The text includes over 200 references and an index. (JB)
Author: Trevor Grant
Author: Trevor Grant
Author: Henrik Brink
Author: Robert Munro
Author: Dawn A. Morley
Author: Laura Saunders
Author: National Research Council
Author: Ted Cuzzillo
Author: Norbert M. Seel
Author: William H. Robertson
Author: Charles C. Bonwell