Deep Active Learning
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Deep Active Learning
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Author : Kayo Matsushita
language : en
Publisher: Springer
Release Date : 2017-09-12
Deep Active Learning written by Kayo Matsushita and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-09-12 with Education categories.
This is the first book to connect the concepts of active learning and deep learning, and to delineate theory and practice through collaboration between scholars in higher education from three countries (Japan, the United States, and Sweden) as well as different subject areas (education, psychology, learning science, teacher training, dentistry, and business).It is only since the beginning of the twenty-first century that active learning has become key to the shift from teaching to learning in Japanese higher education. However, “active learning” in Japan, as in many other countries, is just an umbrella term for teaching methods that promote students’ active participation, such as group work, discussions, presentations, and so on.What is needed for students is not just active learning but deep active learning. Deep learning focuses on content and quality of learning whereas active learning, especially in Japan, focuses on methods of learning. Deep active learning is placed at the intersection of active learning and deep learning, referring to learning that engages students with the world as an object of learning while interacting with others, and helps the students connect what they are learning with their previous knowledge and experiences as well as their future lives.What curricula, pedagogies, assessments and learning environments facilitate such deep active learning? This book attempts to respond to that question by linking theory with practice.
Human In The Loop Machine Learning
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Author : Robert (Munro) Monarch
language : en
Publisher: Simon and Schuster
Release Date : 2021-07-20
Human In The Loop Machine Learning written by Robert (Munro) Monarch 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 2021-07-20 with Computers categories.
Human-in-the-Loop Machine Learning lays out methods for humans and machines to work together effectively. Summary Most machine learning systems that are deployed in the world today learn from human feedback. However, most machine learning courses focus almost exclusively on the algorithms, not the human-computer interaction part of the systems. This can leave a big knowledge gap for data scientists working in real-world machine learning, where data scientists spend more time on data management than on building algorithms. Human-in-the-Loop Machine Learning is a practical guide to optimizing the entire machine learning process, including techniques for annotation, active learning, transfer learning, and using machine learning to optimize every step of the process. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology 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. About the book 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 create 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. What's inside Identifying the right training and evaluation data Finding and managing people to annotate data Selecting annotation quality control strategies Designing interfaces to improve accuracy and efficiency About the author Robert (Munro) Monarch is a data scientist and engineer who has built machine learning data for companies such as Apple, Amazon, Google, and IBM. He holds a PhD from Stanford. Robert holds a PhD from Stanford focused on Human-in-the-Loop machine learning for healthcare and disaster response, and is a disaster response professional in addition to being a machine learning professional. A worked example throughout this text is classifying disaster-related messages from real disasters that Robert has helped respond to in the past. Table of Contents PART 1 - FIRST STEPS 1 Introduction to human-in-the-loop machine learning 2 Getting started with human-in-the-loop machine learning PART 2 - ACTIVE LEARNING 3 Uncertainty sampling 4 Diversity sampling 5 Advanced active learning 6 Applying active learning to different machine learning tasks PART 3 - ANNOTATION 7 Working with the people annotating your data 8 Quality control for data annotation 9 Advanced data annotation and augmentation 10 Annotation quality for different machine learning tasks PART 4 - HUMAN–COMPUTER INTERACTION FOR MACHINE LEARNING 11 Interfaces for data annotation 12 Human-in-the-loop machine learning products
Digital Pathology
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Author : Constantino Carlos Reyes-Aldasoro
language : en
Publisher: Springer
Release Date : 2019-07-03
Digital Pathology written by Constantino Carlos Reyes-Aldasoro and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-07-03 with Computers categories.
This book constitutes the refereed proceedings of the 15th European Congress on Digital Pathology, ECDP 2019, held in Warwick, UK in April 2019. The 21 full papers presented in this volume were carefully reviewed and selected from 30 submissions. The congress theme will be Accelerating Clinical Deployment, with a focus on computational pathology and leveraging the power of big data and artificial intelligence to bridge the gaps between research, development, and clinical uptake.
Active Learning Online
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Author : Stephen Kosslyn
language : en
Publisher:
Release Date : 2021-10-06
Active Learning Online written by Stephen Kosslyn and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-10-06 with categories.
Inspired by the recent proliferation of online courses necessitated by the COVID 19 pandemic, researcher and educational innovator Stephen M. Kosslyn offers instructors and course designers (as well as school administrations and teacher-education students) a treasure trove of active learning principles and activities for implementation in online, hybrid and in-person courses. Whether your course is synchronous (e.g., live with Zoom) or asynchronous (e.g., using video content on Canvas), this book will inject active learning into existing courses or into courses designed from scratch. In both cases, active learning will make the courses not only more interesting but also more effective; student engagement will increase, learning outcomes will be reached, and general teaching and learning experiences will be enriched.
Pattern Recognition And Artificial Intelligence
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Author : Yue Lu
language : en
Publisher: Springer Nature
Release Date : 2020-10-09
Pattern Recognition And Artificial Intelligence written by Yue Lu and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-10-09 with Computers categories.
This book constitutes the proceedings of the Second International Conference on Pattern Recognition and Artificial Intelligence, ICPRAI 2020, which took place in Zhongshan, China, in October 2020. The 49 full and 14 short papers presented were carefully reviewed and selected for inclusion in the book. The papers were organized in topical sections as follows: handwriting and text processing; features and classifiers; deep learning; computer vision and image processing; medical imaging and applications; and forensic studies and medical diagnosis.
Machine Learning In Medical Imaging
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Author : Chunfeng Lian
language : en
Publisher: Springer Nature
Release Date : 2021-09-25
Machine Learning In Medical Imaging written by Chunfeng Lian and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-09-25 with Computers categories.
This book constitutes the proceedings of the 12th International Workshop on Machine Learning in Medical Imaging, MLMI 2021, held in conjunction with MICCAI 2021, in Strasbourg, France, in September 2021.* The 71 papers presented in this volume were carefully reviewed and selected from 92 submissions. They focus on major trends and challenges in the above-mentioned area, aiming to identify new-cutting-edge techniques and their uses in medical imaging. Topics dealt with are: deep learning, generative adversarial learning, ensemble learning, sparse learning, multi-task learning, multi-view learning, manifold learning, and reinforcement learning, with their applications to medical image analysis, computer-aided detection and diagnosis, multi-modality fusion, image reconstruction, image retrieval, cellular image analysis, molecular imaging, digital pathology, etc. *The workshop was held virtually.
Deep Learning For Robot Perception And Cognition
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Author : Alexandros Iosifidis
language : en
Publisher: Academic Press
Release Date : 2022-02-04
Deep Learning For Robot Perception And Cognition written by Alexandros Iosifidis and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-02-04 with Technology & Engineering categories.
Deep Learning for Robot Perception and Cognition introduces a broad range of topics and methods in deep learning for robot perception and cognition together with end-to-end methodologies. The book provides the conceptual and mathematical background needed for approaching a large number of robot perception and cognition tasks from an end-to-end learning point-of-view. The book is suitable for students, university and industry researchers and practitioners in Robotic Vision, Intelligent Control, Mechatronics, Deep Learning, Robotic Perception and Cognition tasks. - Presents deep learning principles and methodologies - Explains the principles of applying end-to-end learning in robotics applications - Presents how to design and train deep learning models - Shows how to apply deep learning in robot vision tasks such as object recognition, image classification, video analysis, and more - Uses robotic simulation environments for training deep learning models - Applies deep learning methods for different tasks ranging from planning and navigation to biosignal analysis
Medical Image Computing And Computer Assisted Intervention Miccai 2020
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Author : Anne L. Martel
language : en
Publisher: Springer Nature
Release Date : 2020-10-02
Medical Image Computing And Computer Assisted Intervention Miccai 2020 written by Anne L. Martel and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-10-02 with Computers categories.
The seven-volume set LNCS 12261, 12262, 12263, 12264, 12265, 12266, and 12267 constitutes the refereed proceedings of the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020, held in Lima, Peru, in October 2020. The conference was held virtually due to the COVID-19 pandemic. The 542 revised full papers presented were carefully reviewed and selected from 1809 submissions in a double-blind review process. The papers are organized in the following topical sections: Part I: machine learning methodologies Part II: image reconstruction; prediction and diagnosis; cross-domain methods and reconstruction; domain adaptation; machine learning applications; generative adversarial networks Part III: CAI applications; image registration; instrumentation and surgical phase detection; navigation and visualization; ultrasound imaging; video image analysis Part IV: segmentation; shape models and landmark detection Part V: biological, optical, microscopic imaging; cell segmentation and stain normalization; histopathology image analysis; opthalmology Part VI: angiography and vessel analysis; breast imaging; colonoscopy; dermatology; fetal imaging; heart and lung imaging; musculoskeletal imaging Part VI: brain development and atlases; DWI and tractography; functional brain networks; neuroimaging; positron emission tomography
Active Learning Theoretical Perspectives Empirical Studies And Design Profiles
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Author : Robert Cassidy
language : en
Publisher: Frontiers Media SA
Release Date : 2019-07-11
Active Learning Theoretical Perspectives Empirical Studies And Design Profiles written by Robert Cassidy and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-07-11 with categories.
This book represents the emerging efforts of a growing international network of researchers and practitioners to promote the development and uptake of evidence-based pedagogies in higher education, at something a level approaching large-scale impact. By offering a communication venue that attracts and enhances much needed partnerships among practitioners and researchers in pedagogical innovation, we aim to change the conversation and focus on how we work and learn together – i.e. extending the implementation and knowledge of co–design methods. In this first edition of our Research Topic on Active Learning, we highlight two (of the three) types of publications we wish to promote. First are studies aimed at understanding the pedagogical designs developed by practitioners in their own practices by bringing to bear the theoretical lenses developed and tested in the education research community. These types of studies constitute the "practice pull" that we see as a necessary counterbalance to "knowledge push" in a more productive pedagogical innovation ecosystem based on research-practitioner partnerships. Second are studies empirically examining the implementations of evidence-based designs in naturalistic settings and under naturalistic conditions. Interestingly, the teams conducting these studies are already exemplars of partnerships between researchers and practitioners who are uniquely positioned as “in-betweens” straddling the two worlds. As a result, these publications represent both the rigours of research and the pragmatism of reflective practice. In forthcoming editions, we will add to this collection a third type of publication -- design profiles. These will present practitioner-developed pedagogical designs at varying levels of abstraction to be held to scrutiny amongst practitioners, instructional designers and researchers alike. We hope by bringing these types of studies together in an open access format that we may contribute to the development of new forms of practitioner-researcher interactions that promote co-design in pedagogical innovation.
Advanced Applied Deep Learning
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Author : Umberto Michelucci
language : en
Publisher: Apress
Release Date : 2019-09-28
Advanced Applied Deep Learning written by Umberto Michelucci and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-09-28 with Computers categories.
Develop and optimize deep learning models with advanced architectures. This book teaches you the intricate details and subtleties of the algorithms that are at the core of convolutional neural networks. In Advanced Applied Deep Learning, you will study advanced topics on CNN and object detection using Keras and TensorFlow. Along the way, you will look at the fundamental operations in CNN, such as convolution and pooling, and then look at more advanced architectures such as inception networks, resnets, and many more. While the book discusses theoretical topics, you will discover how to work efficiently with Keras with many tricks and tips, including how to customize logging in Keras with custom callback classes, what is eager execution, and how to use it in your models. Finally, you will study how object detection works, and build a complete implementation of the YOLO (you only look once) algorithm in Keras and TensorFlow. By the end of the book you will have implemented various models in Keras and learned many advanced tricks that will bring your skills to the next level. What You Will Learn See how convolutional neural networks and object detection work Save weights and models on disk Pause training and restart it at a later stage Use hardware acceleration (GPUs) in your code Work with the Dataset TensorFlow abstraction and use pre-trained models and transfer learning Remove and add layers to pre-trained networks to adapt them to your specific project Apply pre-trained models such as Alexnet and VGG16 to new datasets Who This Book Is For Scientists and researchers with intermediate-to-advanced Python and machine learning know-how. Additionally, intermediate knowledge of Keras and TensorFlow is expected.