From Weakly Supervised Learning To Active Labeling

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Practical Weak Supervision

Author : Wee Hyong Tok
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1492077038
Total Pages : 193 pages
Book Rating : 4.4/5 (92 download)

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Book Synopsis Practical Weak Supervision by : Wee Hyong Tok

Download or read book Practical Weak Supervision written by Wee Hyong Tok and published by "O'Reilly Media, Inc.". This book was released on 2021-09-30 with total page 193 pages. Available in PDF, EPUB and Kindle. Book excerpt: Most data scientists and engineers today rely on quality labeled data to train machine learning models. But building a training set manually is time-consuming and expensive, leaving many companies with unfinished ML projects. There's a more practical approach. In this book, Wee Hyong Tok, Amit Bahree, and Senja Filipi show you how to create products using weakly supervised learning models. You'll learn how to build natural language processing and computer vision projects using weakly labeled datasets from Snorkel, a spin-off from the Stanford AI Lab. Because so many companies have pursued ML projects that never go beyond their labs, this book also provides a guide on how to ship the deep learning models you build. Get up to speed on the field of weak supervision, including ways to use it as part of the data science process Use Snorkel AI for weak supervision and data programming Get code examples for using Snorkel to label text and image datasets Use a weakly labeled dataset for text and image classification Learn practical considerations for using Snorkel with large datasets and using Spark clusters to scale labeling

Spatial Big Data Science

Author : Zhe Jiang
Publisher : Springer
ISBN 13 : 3319601954
Total Pages : 138 pages
Book Rating : 4.3/5 (196 download)

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Book Synopsis Spatial Big Data Science by : Zhe Jiang

Download or read book Spatial Big Data Science written by Zhe Jiang and published by Springer. This book was released on 2017-07-13 with total page 138 pages. Available in PDF, EPUB and Kindle. Book excerpt: Emerging Spatial Big Data (SBD) has transformative potential in solving many grand societal challenges such as water resource management, food security, disaster response, and transportation. However, significant computational challenges exist in analyzing SBD due to the unique spatial characteristics including spatial autocorrelation, anisotropy, heterogeneity, multiple scales and resolutions which is illustrated in this book. This book also discusses current techniques for, spatial big data science with a particular focus on classification techniques for earth observation imagery big data. Specifically, the authors introduce several recent spatial classification techniques, such as spatial decision trees and spatial ensemble learning. Several potential future research directions are also discussed. This book targets an interdisciplinary audience including computer scientists, practitioners and researchers working in the field of data mining, big data, as well as domain scientists working in earth science (e.g., hydrology, disaster), public safety and public health. Advanced level students in computer science will also find this book useful as a reference.

Data Labeling in Machine Learning with Python

Author : Vijaya Kumar Suda
Publisher : Packt Publishing Ltd
ISBN 13 : 1804613789
Total Pages : 398 pages
Book Rating : 4.8/5 (46 download)

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Book Synopsis Data Labeling in Machine Learning with Python by : Vijaya Kumar Suda

Download or read book Data Labeling in Machine Learning with Python written by Vijaya Kumar Suda and published by Packt Publishing Ltd. This book was released on 2024-01-31 with total page 398 pages. Available in PDF, EPUB and Kindle. Book excerpt: Take your data preparation, machine learning, and GenAI skills to the next level by learning a range of Python algorithms and tools for data labeling Key Features Generate labels for regression in scenarios with limited training data Apply generative AI and large language models (LLMs) to explore and label text data Leverage Python libraries for image, video, and audio data analysis and data labeling Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionData labeling is the invisible hand that guides the power of artificial intelligence and machine learning. In today’s data-driven world, mastering data labeling is not just an advantage, it’s a necessity. Data Labeling in Machine Learning with Python empowers you to unearth value from raw data, create intelligent systems, and influence the course of technological evolution. With this book, you'll discover the art of employing summary statistics, weak supervision, programmatic rules, and heuristics to assign labels to unlabeled training data programmatically. As you progress, you'll be able to enhance your datasets by mastering the intricacies of semi-supervised learning and data augmentation. Venturing further into the data landscape, you'll immerse yourself in the annotation of image, video, and audio data, harnessing the power of Python libraries such as seaborn, matplotlib, cv2, librosa, openai, and langchain. With hands-on guidance and practical examples, you'll gain proficiency in annotating diverse data types effectively. By the end of this book, you’ll have the practical expertise to programmatically label diverse data types and enhance datasets, unlocking the full potential of your data.What you will learn Excel in exploratory data analysis (EDA) for tabular, text, audio, video, and image data Understand how to use Python libraries to apply rules to label raw data Discover data augmentation techniques for adding classification labels Leverage K-means clustering to classify unsupervised data Explore how hybrid supervised learning is applied to add labels for classification Master text data classification with generative AI Detect objects and classify images with OpenCV and YOLO Uncover a range of techniques and resources for data annotation Who this book is for This book is for machine learning engineers, data scientists, and data engineers who want to learn data labeling methods and algorithms for model training. Data enthusiasts and Python developers will be able to use this book to learn data exploration and annotation using Python libraries. Basic Python knowledge is beneficial but not necessary to get started.

Medical Image Computing and Computer Assisted Intervention – MICCAI 2022

Author : Linwei Wang
Publisher : Springer Nature
ISBN 13 : 3031164520
Total Pages : 774 pages
Book Rating : 4.0/5 (311 download)

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Book Synopsis Medical Image Computing and Computer Assisted Intervention – MICCAI 2022 by : Linwei Wang

Download or read book Medical Image Computing and Computer Assisted Intervention – MICCAI 2022 written by Linwei Wang and published by Springer Nature. This book was released on 2022-09-15 with total page 774 pages. Available in PDF, EPUB and Kindle. Book excerpt: The eight-volume set LNCS 13431, 13432, 13433, 13434, 13435, 13436, 13437, and 13438 constitutes the refereed proceedings of the 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022, which was held in Singapore in September 2022. The 574 revised full papers presented were carefully reviewed and selected from 1831 submissions in a double-blind review process. The papers are organized in the following topical sections: Part I: Brain development and atlases; DWI and tractography; functional brain networks; neuroimaging; heart and lung imaging; dermatology; Part II: Computational (integrative) pathology; computational anatomy and physiology; ophthalmology; fetal imaging; Part III: Breast imaging; colonoscopy; computer aided diagnosis; Part IV: Microscopic image analysis; positron emission tomography; ultrasound imaging; video data analysis; image segmentation I; Part V: Image segmentation II; integration of imaging with non-imaging biomarkers; Part VI: Image registration; image reconstruction; Part VII: Image-Guided interventions and surgery; outcome and disease prediction; surgical data science; surgical planning and simulation; machine learning – domain adaptation and generalization; Part VIII: Machine learning – weakly-supervised learning; machine learning – model interpretation; machine learning – uncertainty; machine learning theory and methodologies.

Imaging Flow Cytometry

Author : Natasha S. Barteneva
Publisher : Humana
ISBN 13 : 9781493933006
Total Pages : 0 pages
Book Rating : 4.9/5 (33 download)

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Book Synopsis Imaging Flow Cytometry by : Natasha S. Barteneva

Download or read book Imaging Flow Cytometry written by Natasha S. Barteneva and published by Humana. This book was released on 2015-11-23 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This detailed volume for the first time explores techniques and protocols involving quantitative imaging flow cytometry (IFC), which has revolutionized our ability to analyze cells, cellular clusters, and populations in a remarkable fashion. Beginning with an introduction to technology, the book continues with sections addressing protocols for studies on the cell nucleus, nucleic acids, and FISH techniques using an IFC instrument, immune response analysis and drug screening, IFC protocols for apoptosis and cell death analysis, as well as morphological analysis and the identification of rare cells. Written for the highly successful Methods in Molecular Biology series, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls. Authoritative and practical, Imaging Flow Cytometry: Methods and Protocols will be a critical source for all laboratories seeking to implement IFC in their research studies.

Semantic Systems. The Power of AI and Knowledge Graphs

Author : Maribel Acosta
Publisher : Springer Nature
ISBN 13 : 3030332209
Total Pages : 400 pages
Book Rating : 4.0/5 (33 download)

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Book Synopsis Semantic Systems. The Power of AI and Knowledge Graphs by : Maribel Acosta

Download or read book Semantic Systems. The Power of AI and Knowledge Graphs written by Maribel Acosta and published by Springer Nature. This book was released on 2019-11-04 with total page 400 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book constitutes the refereed proceedings of the 15th International Conference on Semantic Systems, SEMANTiCS 2019, held in Karlsruhe, Germany, in September 2019. The 20 full papers and 8 short papers presented in this volume were carefully reviewed and selected from 88 submissions. They cover topics such as: web semantics and linked (open) data; machine learning and deep learning techniques; semantic information management and knowledge integration; terminology, thesaurus and ontology management; data mining and knowledge discovery; semantics in blockchain and distributed ledger technologies.

Interpretable and Annotation-Efficient Learning for Medical Image Computing

Author : Jaime Cardoso
Publisher : Springer Nature
ISBN 13 : 3030611663
Total Pages : 292 pages
Book Rating : 4.0/5 (36 download)

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Book Synopsis Interpretable and Annotation-Efficient Learning for Medical Image Computing by : Jaime Cardoso

Download or read book Interpretable and Annotation-Efficient Learning for Medical Image Computing written by Jaime Cardoso and published by Springer Nature. This book was released on 2020-10-03 with total page 292 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed joint proceedings of the Third International Workshop on Interpretability of Machine Intelligence in Medical Image Computing, iMIMIC 2020, the Second International Workshop on Medical Image Learning with Less Labels and Imperfect Data, MIL3ID 2020, and the 5th International Workshop on Large-scale Annotation of Biomedical data and Expert Label Synthesis, LABELS 2020, held in conjunction with the 23rd International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2020, in Lima, Peru, in October 2020. The 8 full papers presented at iMIMIC 2020, 11 full papers to MIL3ID 2020, and the 10 full papers presented at LABELS 2020 were carefully reviewed and selected from 16 submissions to iMIMIC, 28 to MIL3ID, and 12 submissions to LABELS. The iMIMIC papers focus on introducing the challenges and opportunities related to the topic of interpretability of machine learning systems in the context of medical imaging and computer assisted intervention. MIL3ID deals with best practices in medical image learning with label scarcity and data imperfection. The LABELS papers present a variety of approaches for dealing with a limited number of labels, from semi-supervised learning to crowdsourcing.

Computer Vision – ECCV 2020

Author : Andrea Vedaldi
Publisher : Springer Nature
ISBN 13 : 3030585743
Total Pages : 830 pages
Book Rating : 4.0/5 (35 download)

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Book Synopsis Computer Vision – ECCV 2020 by : Andrea Vedaldi

Download or read book Computer Vision – ECCV 2020 written by Andrea Vedaldi and published by Springer Nature. This book was released on 2020-11-12 with total page 830 pages. Available in PDF, EPUB and Kindle. Book excerpt: The 30-volume set, comprising the LNCS books 12346 until 12375, constitutes the refereed proceedings of the 16th European Conference on Computer Vision, ECCV 2020, which was planned to be held in Glasgow, UK, during August 23-28, 2020. The conference was held virtually due to the COVID-19 pandemic. The 1360 revised papers presented in these proceedings were carefully reviewed and selected from a total of 5025 submissions. The papers deal with topics such as computer vision; machine learning; deep neural networks; reinforcement learning; object recognition; image classification; image processing; object detection; semantic segmentation; human pose estimation; 3d reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; object recognition; motion estimation.

Computer Vision – ECCV 2022

Author : Shai Avidan
Publisher : Springer Nature
ISBN 13 : 303120056X
Total Pages : 801 pages
Book Rating : 4.0/5 (312 download)

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Book Synopsis Computer Vision – ECCV 2022 by : Shai Avidan

Download or read book Computer Vision – ECCV 2022 written by Shai Avidan and published by Springer Nature. This book was released on 2022-11-02 with total page 801 pages. Available in PDF, EPUB and Kindle. Book excerpt: The 39-volume set, comprising the LNCS books 13661 until 13699, constitutes the refereed proceedings of the 17th European Conference on Computer Vision, ECCV 2022, held in Tel Aviv, Israel, during October 23–27, 2022. The 1645 papers presented in these proceedings were carefully reviewed and selected from a total of 5804 submissions. The papers deal with topics such as computer vision; machine learning; deep neural networks; reinforcement learning; object recognition; image classification; image processing; object detection; semantic segmentation; human pose estimation; 3d reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; object recognition; motion estimation.

Crowdsourcing in Computer Vision

Author : Adriana Kovashka
Publisher :
ISBN 13 : 9781680832129
Total Pages : 80 pages
Book Rating : 4.8/5 (321 download)

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Book Synopsis Crowdsourcing in Computer Vision by : Adriana Kovashka

Download or read book Crowdsourcing in Computer Vision written by Adriana Kovashka and published by . This book was released on 2016-11-30 with total page 80 pages. Available in PDF, EPUB and Kindle. Book excerpt: An overview of how crowdsourcing has been used in computer vision, enabling a computer vision researcher who has previously not collected non-expert data to devise a data collection strategy. It will also be of help to researchers who focus broadly on crowdsourcing to examine how the latter has been applied in computer vision.

Pattern Recognition and Computer Vision

Author : Huimin Ma
Publisher : Springer Nature
ISBN 13 : 3030880079
Total Pages : 695 pages
Book Rating : 4.0/5 (38 download)

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Book Synopsis Pattern Recognition and Computer Vision by : Huimin Ma

Download or read book Pattern Recognition and Computer Vision written by Huimin Ma and published by Springer Nature. This book was released on 2021-10-22 with total page 695 pages. Available in PDF, EPUB and Kindle. Book excerpt: The 4-volume set LNCS 13019, 13020, 13021 and 13022 constitutes the refereed proceedings of the 4th Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2021, held in Beijing, China, in October-November 2021. The 201 full papers presented were carefully reviewed and selected from 513 submissions. The papers have been organized in the following topical sections: Object Detection, Tracking and Recognition; Computer Vision, Theories and Applications, Multimedia Processing and Analysis; Low-level Vision and Image Processing; Biomedical Image Processing and Analysis; Machine Learning, Neural Network and Deep Learning, and New Advances in Visual Perception and Understanding.

Introduction to Semi-Supervised Learning

Author : Xiaojin Geffner
Publisher : Springer Nature
ISBN 13 : 3031015487
Total Pages : 116 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis Introduction to Semi-Supervised Learning by : Xiaojin Geffner

Download or read book Introduction to Semi-Supervised Learning written by Xiaojin Geffner and published by Springer Nature. This book was released on 2022-05-31 with total page 116 pages. Available in PDF, EPUB and Kindle. Book excerpt: Semi-supervised learning is a learning paradigm concerned with the study of how computers and natural systems such as humans learn in the presence of both labeled and unlabeled data. Traditionally, learning has been studied either in the unsupervised paradigm (e.g., clustering, outlier detection) where all the data are unlabeled, or in the supervised paradigm (e.g., classification, regression) where all the data are labeled. The goal of semi-supervised learning is to understand how combining labeled and unlabeled data may change the learning behavior, and design algorithms that take advantage of such a combination. Semi-supervised learning is of great interest in machine learning and data mining because it can use readily available unlabeled data to improve supervised learning tasks when the labeled data are scarce or expensive. Semi-supervised learning also shows potential as a quantitative tool to understand human category learning, where most of the input is self-evidently unlabeled. In this introductory book, we present some popular semi-supervised learning models, including self-training, mixture models, co-training and multiview learning, graph-based methods, and semi-supervised support vector machines. For each model, we discuss its basic mathematical formulation. The success of semi-supervised learning depends critically on some underlying assumptions. We emphasize the assumptions made by each model and give counterexamples when appropriate to demonstrate the limitations of the different models. In addition, we discuss semi-supervised learning for cognitive psychology. Finally, we give a computational learning theoretic perspective on semi-supervised learning, and we conclude the book with a brief discussion of open questions in the field. Table of Contents: Introduction to Statistical Machine Learning / Overview of Semi-Supervised Learning / Mixture Models and EM / Co-Training / Graph-Based Semi-Supervised Learning / Semi-Supervised Support Vector Machines / Human Semi-Supervised Learning / Theory and Outlook

Semi-Supervised Learning

Author : Olivier Chapelle
Publisher : MIT Press
ISBN 13 : 0262514125
Total Pages : 525 pages
Book Rating : 4.2/5 (625 download)

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Book Synopsis Semi-Supervised Learning by : Olivier Chapelle

Download or read book Semi-Supervised Learning written by Olivier Chapelle and published by MIT Press. This book was released on 2010-01-22 with total page 525 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive review of an area of machine learning that deals with the use of unlabeled data in classification problems: state-of-the-art algorithms, a taxonomy of the field, applications, benchmark experiments, and directions for future research. In the field of machine learning, semi-supervised learning (SSL) occupies the middle ground, between supervised learning (in which all training examples are labeled) and unsupervised learning (in which no label data are given). Interest in SSL has increased in recent years, particularly because of application domains in which unlabeled data are plentiful, such as images, text, and bioinformatics. This first comprehensive overview of SSL presents state-of-the-art algorithms, a taxonomy of the field, selected applications, benchmark experiments, and perspectives on ongoing and future research.Semi-Supervised Learning first presents the key assumptions and ideas underlying the field: smoothness, cluster or low-density separation, manifold structure, and transduction. The core of the book is the presentation of SSL methods, organized according to algorithmic strategies. After an examination of generative models, the book describes algorithms that implement the low-density separation assumption, graph-based methods, and algorithms that perform two-step learning. The book then discusses SSL applications and offers guidelines for SSL practitioners by analyzing the results of extensive benchmark experiments. Finally, the book looks at interesting directions for SSL research. The book closes with a discussion of the relationship between semi-supervised learning and transduction.

On-Chip Training NPU - Algorithm, Architecture and SoC Design

Author : Donghyeon Han
Publisher : Springer Nature
ISBN 13 : 3031342372
Total Pages : 249 pages
Book Rating : 4.0/5 (313 download)

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Book Synopsis On-Chip Training NPU - Algorithm, Architecture and SoC Design by : Donghyeon Han

Download or read book On-Chip Training NPU - Algorithm, Architecture and SoC Design written by Donghyeon Han and published by Springer Nature. This book was released on 2023-08-28 with total page 249 pages. Available in PDF, EPUB and Kindle. Book excerpt: Unlike most available sources that focus on deep neural network (DNN) inference, this book provides readers with a single-source reference on the needs, requirements, and challenges involved with on-device, DNN training semiconductor and SoC design. The authors include coverage of the trends and history surrounding the development of on-device DNN training, as well as on-device training semiconductors and SoC design examples to facilitate understanding.

Supervised and Unsupervised Learning for Data Science

Author : Michael W. Berry
Publisher : Springer Nature
ISBN 13 : 3030224759
Total Pages : 191 pages
Book Rating : 4.0/5 (32 download)

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Book Synopsis Supervised and Unsupervised Learning for Data Science by : Michael W. Berry

Download or read book Supervised and Unsupervised Learning for Data Science written by Michael W. Berry and published by Springer Nature. This book was released on 2019-09-04 with total page 191 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers the state of the art in learning algorithms with an inclusion of semi-supervised methods to provide a broad scope of clustering and classification solutions for big data applications. Case studies and best practices are included along with theoretical models of learning for a comprehensive reference to the field. The book is organized into eight chapters that cover the following topics: discretization, feature extraction and selection, classification, clustering, topic modeling, graph analysis and applications. Practitioners and graduate students can use the volume as an important reference for their current and future research and faculty will find the volume useful for assignments in presenting current approaches to unsupervised and semi-supervised learning in graduate-level seminar courses. The book is based on selected, expanded papers from the Fourth International Conference on Soft Computing in Data Science (2018). Includes new advances in clustering and classification using semi-supervised and unsupervised learning; Address new challenges arising in feature extraction and selection using semi-supervised and unsupervised learning; Features applications from healthcare, engineering, and text/social media mining that exploit techniques from semi-supervised and unsupervised learning.

Five-Layer Intelligence of the Machine Brain

Author : Wen-Feng Wang
Publisher : Springer Nature
ISBN 13 : 9811902720
Total Pages : 223 pages
Book Rating : 4.8/5 (119 download)

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Book Synopsis Five-Layer Intelligence of the Machine Brain by : Wen-Feng Wang

Download or read book Five-Layer Intelligence of the Machine Brain written by Wen-Feng Wang and published by Springer Nature. This book was released on 2022-03-15 with total page 223 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book intends to report the new results of the efforts on the study of Layered Intelligence of the Machine Brain (LIMB). The book collects novel research ideas in LIMB and summarizes the current machine intelligence level as “five layer intelligence”- environments sensing, active learning, cognitive computing, intelligent decision making and automatized execution. The book is likely to be of interest to university researchers, R&D engineers and graduate students in computer science and electronics who wish to learn the core principles, methods, algorithms, and applications of LIMB.

Data-Centric Machine Learning with Python

Author : Jonas Christensen
Publisher : Packt Publishing Ltd
ISBN 13 : 1804612413
Total Pages : 378 pages
Book Rating : 4.8/5 (46 download)

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Book Synopsis Data-Centric Machine Learning with Python by : Jonas Christensen

Download or read book Data-Centric Machine Learning with Python written by Jonas Christensen and published by Packt Publishing Ltd. This book was released on 2024-02-29 with total page 378 pages. Available in PDF, EPUB and Kindle. Book excerpt: Join the data-centric revolution and master the concepts, techniques, and algorithms shaping the future of AI and ML development, using Python Key Features Grasp the principles of data centricity and apply them to real-world scenarios Gain experience with quality data collection, labeling, and synthetic data creation using Python Develop essential skills for building reliable, responsible, and ethical machine learning solutions Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionIn the rapidly advancing data-driven world where data quality is pivotal to the success of machine learning and artificial intelligence projects, this critically timed guide provides a rare, end-to-end overview of data-centric machine learning (DCML), along with hands-on applications of technical and non-technical approaches to generating deeper and more accurate datasets. This book will help you understand what data-centric ML/AI is and how it can help you to realize the potential of ‘small data’. Delving into the building blocks of data-centric ML/AI, you’ll explore the human aspects of data labeling, tackle ambiguity in labeling, and understand the role of synthetic data. From strategies to improve data collection to techniques for refining and augmenting datasets, you’ll learn everything you need to elevate your data-centric practices. Through applied examples and insights for overcoming challenges, you’ll get a roadmap for implementing data-centric ML/AI in diverse applications in Python. By the end of this book, you’ll have developed a profound understanding of data-centric ML/AI and the proficiency to seamlessly integrate common data-centric approaches in the model development lifecycle to unlock the full potential of your machine learning projects by prioritizing data quality and reliability.What you will learn Understand the impact of input data quality compared to model selection and tuning Recognize the crucial role of subject-matter experts in effective model development Implement data cleaning, labeling, and augmentation best practices Explore common synthetic data generation techniques and their applications Apply synthetic data generation techniques using common Python packages Detect and mitigate bias in a dataset using best-practice techniques Understand the importance of reliability, responsibility, and ethical considerations in ML/AI Who this book is for This book is for data science professionals and machine learning enthusiasts looking to understand the concept of data-centricity, its benefits over a model-centric approach, and the practical application of a best-practice data-centric approach in their work. This book is also for other data professionals and senior leaders who want to explore the tools and techniques to improve data quality and create opportunities for small data ML/AI in their organizations.