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Optimal Learning

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Optimal Learning

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Author : Warren B. Powell
language : en
Publisher: John Wiley & Sons
Release Date : 2013-07-09

Optimal Learning written by Warren B. Powell and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-07-09 with Mathematics categories.

Learn the science of collecting information to make effective decisions Everyday decisions are made without the benefit of accurate information. Optimal Learning develops the needed principles for gathering information to make decisions, especially when collecting information is time-consuming and expensive. Designed for readers with an elementary background in probability and statistics, the book presents effective and practical policies illustrated in a wide range of applications, from energy, homeland security, and transportation to engineering, health, and business. This book covers the fundamental dimensions of a learning problem and presents a simple method for testing and comparing policies for learning. Special attention is given to the knowledge gradient policy and its use with a wide range of belief models, including lookup table and parametric and for online and offline problems. Three sections develop ideas with increasing levels of sophistication: Fundamentals explores fundamental topics, including adaptive learning, ranking and selection, the knowledge gradient, and bandit problems Extensions and Applications features coverage of linear belief models, subset selection models, scalar function optimization, optimal bidding, and stopping problems Advanced Topics explores complex methods including simulation optimization, active learning in mathematical programming, and optimal continuous measurements Each chapter identifies a specific learning problem, presents the related, practical algorithms for implementation, and concludes with numerous exercises. A related website features additional applications and downloadable software, including MATLAB and the Optimal Learning Calculator, a spreadsheet-based package that provides an introduction to learning and a variety of policies for learning.

Optimal Learning

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Author : Warren B. Powell
language : en
Publisher: John Wiley & Sons
Release Date : 2012-04-17

Optimal Learning Environments To Promote Student Engagement

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Author : David J. Shernoff
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-05-29

Optimal Learning Environments To Promote Student Engagement written by David J. Shernoff and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-05-29 with Psychology categories.

Optimal Learning Environments to Promote Student Engagement analyzes the psychological, social, and academic phenomena comprising engagement, framing it as critical to learning and development. Drawing on positive psychology, flow studies, and theories of motivation, the book conceptualizes engagement as a learning experience, explaining how it occurs (or not) and how schools can adapt to maximize it among adolescents. Examples of empirically supported environments promoting engagement are provided, representing alternative high schools, Montessori schools, and extracurricular programs. The book identifies key innovations including community-school partnerships, technology-supported learning, and the potential for engaging learning opportunities during an expanded school day. Among the topics covered: Engagement as a primary framework for understanding educational and motivational outcomes. Measuring the malleability, complexity, multidimensionality, and sources of engagement. The relationship between engagement and achievement. Supporting and challenging: the instructor’s role in promoting engagement. Engagement within and beyond core academic subjects. Technological innovations on the engagement horizon. Optimal Learning Environments to Promote Student Engagement is an essential resource for researchers, professionals, and graduate students in child and school psychology; social work; educational psychology; positive psychology; family studies; and teaching/teacher education.

Optimizing Learning Outcomes

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Author : William Steele
language : en
Publisher: Routledge
Release Date : 2017-02-24

Optimizing Learning Outcomes written by William Steele and has been published by Routledge this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-02-24 with Psychology categories.

Optimizing Learning Outcomes provides answers for the most pressing questions that mental health professionals, teachers, and administrators are facing in today’s schools. Chapters provide a wide array of evidence-based resources—including links to video segments—that promote understanding, discussion, and successful modeling. Accessible how-to trainings provide readers with multiple sensory-based practices that improve academic success and promote behavioral regulation. Clinicians and educators will come away from this book with a variety of tools for facilitating brain-based, trauma-sensitive learning for all, realizing improved learning outcomes, improving teacher satisfaction, and reducing disciplinary actions and suspensions.

Wealth Without A Job

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Author : Phil Laut
language : en
Publisher: John Wiley & Sons
Release Date : 2004-09-27

Wealth Without A Job written by Phil Laut and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004-09-27 with Business & Economics categories.

Strategies for successful business ownership Job security has become a myth, no matter where you work. Alan Greenspan testified before the House Ways and Means Committee in February 2004 that there is a turnover rate of approximately 1 million jobs per month. Because of the turbulent job market, more and more people are concluding that entrepreneurship is the way to go. However, many jobholders view entrepreneurship as risky. Entrepreneurship isn’ t taught in schools and most entrepreneurs endure a baptism by fire before they taste success. This book shows you learn not only how to strike out on your own, but how to make your entrepreneurial dream a success. Phil Laut (Charlotte, NC), self-published Money Is My Friend in 1979 and sold 400,000 copies in 18 languages. Andy Fuehl (Phoenix, AZ) is a recognized teacher of business psychology. He has also worked for several Fortune 500 companies such as AT& T, Bell Labs, and Motorola. Mr. Fuehl went from unemployed to self-made millionaire in less than three years using the methods in this book.

Neural Networks Computational Models And Applications

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Author : Huajin Tang
language : en
Publisher: Springer Science & Business Media
Release Date : 2007-03-12

Neural Networks Computational Models And Applications written by Huajin Tang and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-03-12 with Computers categories.

Neural Networks: Computational Models and Applications presents important theoretical and practical issues in neural networks, including the learning algorithms of feed-forward neural networks, various dynamical properties of recurrent neural networks, winner-take-all networks and their applications in broad manifolds of computational intelligence: pattern recognition, uniform approximation, constrained optimization, NP-hard problems, and image segmentation. The book offers a compact, insightful understanding of the broad and rapidly growing neural networks domain.

Deep Learning For Vision Systems

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Author : Mohamed Elgendy
language : en
Publisher: Manning
Release Date : 2020-11-10

Deep Learning For Vision Systems written by Mohamed Elgendy and has been published by Manning this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-10 with Computers categories.

How does the computer learn to understand what it sees? Deep Learning for Vision Systems answers that by applying deep learning to computer vision. Using only high school algebra, this book illuminates the concepts behind visual intuition. You'll understand how to use deep learning architectures to build vision system applications for image generation and facial recognition. Summary Computer vision is central to many leading-edge innovations, including self-driving cars, drones, augmented reality, facial recognition, and much, much more. Amazing new computer vision applications are developed every day, thanks to rapid advances in AI and deep learning (DL). Deep Learning for Vision Systems teaches you the concepts and tools for building intelligent, scalable computer vision systems that can identify and react to objects in images, videos, and real life. With author Mohamed Elgendy's expert instruction and illustration of real-world projects, you’ll finally grok state-of-the-art deep learning techniques, so you can build, contribute to, and lead in the exciting realm of computer vision! Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology How much has computer vision advanced? One ride in a Tesla is the only answer you’ll need. Deep learning techniques have led to exciting breakthroughs in facial recognition, interactive simulations, and medical imaging, but nothing beats seeing a car respond to real-world stimuli while speeding down the highway. About the book How does the computer learn to understand what it sees? Deep Learning for Vision Systems answers that by applying deep learning to computer vision. Using only high school algebra, this book illuminates the concepts behind visual intuition. You'll understand how to use deep learning architectures to build vision system applications for image generation and facial recognition. What's inside Image classification and object detection Advanced deep learning architectures Transfer learning and generative adversarial networks DeepDream and neural style transfer Visual embeddings and image search About the reader For intermediate Python programmers. About the author Mohamed Elgendy is the VP of Engineering at Rakuten. A seasoned AI expert, he has previously built and managed AI products at Amazon and Twilio. Table of Contents PART 1 - DEEP LEARNING FOUNDATION 1 Welcome to computer vision 2 Deep learning and neural networks 3 Convolutional neural networks 4 Structuring DL projects and hyperparameter tuning PART 2 - IMAGE CLASSIFICATION AND DETECTION 5 Advanced CNN architectures 6 Transfer learning 7 Object detection with R-CNN, SSD, and YOLO PART 3 - GENERATIVE MODELS AND VISUAL EMBEDDINGS 8 Generative adversarial networks (GANs) 9 DeepDream and neural style transfer 10 Visual embeddings

Transfer Learning For Natural Language Processing

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Author : Paul Azunre
language : en
Publisher: Simon and Schuster
Release Date : 2021-08-31

Transfer Learning For Natural Language Processing written by Paul Azunre and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-08-31 with Computers categories.

Build custom NLP models in record time by adapting pre-trained machine learning models to solve specialized problems. Summary In Transfer Learning for Natural Language Processing you will learn: Fine tuning pretrained models with new domain data Picking the right model to reduce resource usage Transfer learning for neural network architectures Generating text with generative pretrained transformers Cross-lingual transfer learning with BERT Foundations for exploring NLP academic literature Training deep learning NLP models from scratch is costly, time-consuming, and requires massive amounts of data. In Transfer Learning for Natural Language Processing, DARPA researcher Paul Azunre reveals cutting-edge transfer learning techniques that apply customizable pretrained models to your own NLP architectures. You’ll learn how to use transfer learning to deliver state-of-the-art results for language comprehension, even when working with limited label data. Best of all, you’ll save on training time and computational costs. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Build custom NLP models in record time, even with limited datasets! Transfer learning is a machine learning technique for adapting pretrained machine learning models to solve specialized problems. This powerful approach has revolutionized natural language processing, driving improvements in machine translation, business analytics, and natural language generation. About the book Transfer Learning for Natural Language Processing teaches you to create powerful NLP solutions quickly by building on existing pretrained models. This instantly useful book provides crystal-clear explanations of the concepts you need to grok transfer learning along with hands-on examples so you can practice your new skills immediately. As you go, you’ll apply state-of-the-art transfer learning methods to create a spam email classifier, a fact checker, and more real-world applications. What's inside Fine tuning pretrained models with new domain data Picking the right model to reduce resource use Transfer learning for neural network architectures Generating text with pretrained transformers About the reader For machine learning engineers and data scientists with some experience in NLP. About the author Paul Azunre holds a PhD in Computer Science from MIT and has served as a Principal Investigator on several DARPA research programs. Table of Contents PART 1 INTRODUCTION AND OVERVIEW 1 What is transfer learning? 2 Getting started with baselines: Data preprocessing 3 Getting started with baselines: Benchmarking and optimization PART 2 SHALLOW TRANSFER LEARNING AND DEEP TRANSFER LEARNING WITH RECURRENT NEURAL NETWORKS (RNNS) 4 Shallow transfer learning for NLP 5 Preprocessing data for recurrent neural network deep transfer learning experiments 6 Deep transfer learning for NLP with recurrent neural networks PART 3 DEEP TRANSFER LEARNING WITH TRANSFORMERS AND ADAPTATION STRATEGIES 7 Deep transfer learning for NLP with the transformer and GPT 8 Deep transfer learning for NLP with BERT and multilingual BERT 9 ULMFiT and knowledge distillation adaptation strategies 10 ALBERT, adapters, and multitask adaptation strategies 11 Conclusions

Navigating Place Based Learning

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Author : Elizabeth Langran
language : en
Publisher: Springer Nature
Release Date : 2020-11-04

Navigating Place Based Learning written by Elizabeth Langran and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-04 with Education categories.

This book explores how educators can realize the potential of critical place-based pedagogy. The authors’ model leverages the power of technology through strategies such as mobile mapping so that students can read the world and share spatial narratives. The same complexity that makes spaces outside the classroom ideal for authentic, purposeful learning creates challenges for educators who must minimize students taking wrong turns or reaching dead ends. Instructional design process is key and the authors offer exemplars of this from multiple disciplines. Whether students are exploring a local community or a natural environment, place-based inquires must include recognition of privilege and the social dynamics that reinforce inequalities. Concluding with a discussion of the changing social context, the authors highlight how contemporary events add a sense of urgency to the call for a critical place-based pedagogy—one that is more inclusive for all students.

Macroeconomics At The Service Of Public Policy

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Author : Thomas J. Sargent
language : en
Publisher: OUP Oxford
Release Date : 2013-02-28

Macroeconomics At The Service Of Public Policy written by Thomas J. Sargent and has been published by OUP Oxford this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-02-28 with Business & Economics categories.

This volume uses state of the art models from the frontier of macroeconomics to answer key questions about how the economy functions and how policy should be conducted. The contributions cover a wide range of issues in macroeconomics and macroeconomic policy. They combine high level mathematics with economic analysis, and highlight the need to update our mathematical toolbox in order to understand the increased complexity of the macroeconomic environment. The volume represents hard evidence of high research intensity in many fields of macroeconomics, and warns against interpreting the scope of macroeconomics too narrowly. The mainstream business cycle analysis, based on dynamic stochastic general equilibrium (DSGE) modelling of a particular type, has been criticised for its inability to predict or resolve the recent financial crisis. However, macroeconomic research on financial, information, and learning imperfections had not yet made their way into many of the pre-crisis DSGE models because practical econometric versions of those models were mainly designed to fit data periods that did not include financial crises. A major response to the limitations of those older DSGE models is an active research program to bring big financial shocks and various kinds of financial, learning, and labour market frictions into a new generation of DSGE models for guiding policy. The contributors to this book utilise models and modelling assumptions that go beyond particular modelling conventions. By using alternative yet plausible assumptions, they seek to enrich our knowledge and ability to explain macroeconomic phenomena. They contribute to expanding the frontier of macroeconomic knowledge in ways that will prove useful for macroeconomic policy.

Optimal Learning

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