Training Data For Machine Learning
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Training Data For Machine Learning
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Author : Anthony Sarkis
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2023-11-08
Training Data For Machine Learning written by Anthony Sarkis and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-11-08 with Computers categories.
Your training data has as much to do with the success of your data project as the algorithms themselves because most failures in AI systems relate to training data. But while training data is the foundation for successful AI and machine learning, there are few comprehensive resources to help you ace the process. In this hands-on guide, author Anthony Sarkis--lead engineer for the Diffgram AI training data software--shows technical professionals, managers, and subject matter experts how to work with and scale training data, while illuminating the human side of supervising machines. Engineering leaders, data engineers, and data science professionals alike will gain a solid understanding of the concepts, tools, and processes they need to succeed with training data. With this book, you'll learn how to: Work effectively with training data including schemas, raw data, and annotations Transform your work, team, or organization to be more AI/ML data-centric Clearly explain training data concepts to other staff, team members, and stakeholders Design, deploy, and ship training data for production-grade AI applications Recognize and correct new training-data-based failure modes such as data bias Confidently use automation to more effectively create training data Successfully maintain, operate, and improve training data systems of record
Training Data For Machine Learning Models
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Author : Safari, an O'Reilly Media Company
language : en
Publisher:
Release Date : 2022
Training Data For Machine Learning Models written by Safari, an O'Reilly Media Company and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with categories.
Your training data has as much to do with the success of your data project as the algorithms themselves--most failures in deep learning systems relate to training data. But while training data is the foundation for successful machine learning, there are few comprehensive resources to help you ace the process. This hands-on guide explains how to work with and scale training data. You'll gain a solid understanding of the concepts, tools, and processes needed to: Design, deploy, and ship training data for production-grade deep learning applications Integrate with a growing ecosystem of tools Recognize and correct new training data-based failure modes Improve existing system performance and avoid development risks Confidently use automation and acceleration approaches to more effectively create training data Avoid data loss by structuring metadata around created datasets Clearly explain training data concepts to subject matter experts and other shareholders Successfully maintain, operate, and improve your system.
Training Data For Machine Learning
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Author : Anthony Sarkis
language : en
Publisher:
Release Date : 2024
Training Data For Machine Learning written by Anthony Sarkis and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024 with Business categories.
Accelerating Machine Learning With Training Data Management
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Author : Alexander Jason Ratner
language : en
Publisher:
Release Date : 2019
Accelerating Machine Learning With Training Data Management written by Alexander Jason Ratner and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with categories.
One of the biggest bottlenecks in developing machine learning applications today is the need for large hand-labeled training datasets. Even at the world's most sophisticated technology companies, and especially at other organizations across science, medicine, industry, and government, the time and monetary cost of labeling and managing large training datasets is often the blocking factor in using machine learning. In this thesis, we describe work on training data management systems that enable users to programmatically build and manage training datasets, rather than labeling and managing them by hand, and present algorithms and supporting theory for automatically modeling this noisier process of training set specification in order to improve the resulting training set quality. We then describe extensive empirical results and real-world deployments demonstrating that programmatically building, managing, and modeling training sets in this way can lead to radically faster, more flexible, and more accessible ways of developing machine learning applications. We start by describing data programming, a paradigm for labeling training datasets programmatically rather than by hand, and Snorkel, an open source training data management system built around data programming that has been used by major technology companies, academic labs, and government agencies to build machine learning applications in days or weeks rather than months or years. In Snorkel, rather than hand-labeling training data, users write programmatic operators called labeling functions, which label data using various heuristic or weak supervision strategies such as pattern matching, distant supervision, and other models. These labeling functions can have noisy, conflicting, and correlated outputs, which Snorkel models and combines into clean training labels without requiring any ground truth using theoretically consistent modeling approaches we develop. We then report on extensive empirical validations, user studies, and real-world applications of Snorkel in industrial, scientific, medical, and other use cases ranging from knowledge base construction from text data to medical monitoring over image and video data. Next, we will describe two other approaches for enabling users to programmatically build and manage training datasets, both currently integrated into the Snorkel open source framework: Snorkel MeTaL, an extension of data programming and Snorkel to the setting where users have multiple related classification tasks, in particular focusing on multi-task learning; and TANDA, a system for optimizing and managing strategies for data augmentation, a critical training dataset management technique wherein a labeled dataset is artificially expanded by transforming data points. Finally, we will conclude by outlining future research directions for further accelerating and democratizing machine learning workflows, such as higher-level programmatic interfaces and massively multi-task frameworks.
Collins International Lower Secondary Computing International Lower Secondary Computing Student S Book Stage 7
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Author : Dr Tracy Gardner
language : en
Publisher: HarperCollins UK
Release Date : 2024-11-29
Collins International Lower Secondary Computing International Lower Secondary Computing Student S Book Stage 7 written by Dr Tracy Gardner and has been published by HarperCollins UK this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-11-29 with Juvenile Nonfiction categories.
Collins International Lower Secondary Computing series is structured around six themes relating to computing and digital literacy and offers learners a comprehensive and engaging approach to learning, enhanced by real-world applications. Each chapter is organised to develop essential skills, leading learners towards the creation of a final project.
Applied Machine Learning
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Author : M. Gopal
language : en
Publisher: McGraw Hill Professional
Release Date : 2019-06-07
Applied Machine Learning written by M. Gopal and has been published by McGraw Hill Professional this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-06-07 with Technology & Engineering categories.
Cutting-edge machine learning principles, practices, and applications This comprehensive textbook explores the theoretical under¬pinnings of learning and equips readers with the knowledge needed to apply powerful machine learning techniques to solve challenging real-world problems. Applied Machine Learning shows, step by step, how to conceptualize problems, accurately represent data, select and tune algorithms, interpret and analyze results, and make informed strategic decisions. Presented in a non-rigorous mathematical style, the book covers a broad array of machine learning topics with special emphasis on methods that have been profitably employed. Coverage includes: Supervised learning Statistical learning Learning with support vector machines (SVM) Learning with neural networks (NN) Fuzzy inference systems Data clustering Data transformations Decision tree learning Business intelligence Data mining And much more
Collins International Lower Secondary Computing International Lower Secondary Computing Teacher S Guide Stage 7
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Author : Dr Tracy Gardner
language : en
Publisher: HarperCollins UK
Release Date : 2024-11-29
Collins International Lower Secondary Computing International Lower Secondary Computing Teacher S Guide Stage 7 written by Dr Tracy Gardner and has been published by HarperCollins UK this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-11-29 with Juvenile Nonfiction categories.
Collins International Lower Secondary Computing series provides in-depth coverage of the Cambridge curriculum frameworks Lower Secondary Computing (0860) and Lower Secondary Digital Literacy (0082), as well as the UK national curriculum for Computing at secondary level. The course is organised around six themes relating to computing and digital literacy – Our digital world; Content creation; Create with code; How computers work; Connect the world; The power of data. The chapter overviews and lesson summaries show how these themes are developed in Stage 7, helping teachers to plan their course. The Teacher’s Guide offers a detailed lesson plan for every unit, giving guidance on how to run the session, including lesson objectives, reference to key pedagogies, mapping grids, worksheets, resource sheets, answers, support for assessing end-of-chapter projects and ‘Build on this’ extension activities. The lesson plans and introduction provide valuable subject knowledge and support for non-specialists. This includes summaries of the key computing pedagogies used in the resources, and references to further reading to develop teachers’ own practice. Built around an internationally sound pedagogy, this framework for teaching provides real-life contexts and culturally responsive examples into a project-based approach and incorporates all learners’ knowledge, ways of learning, and heritage; where learners are given more of an opportunity to choose appropriate contexts for themselves. The accompanying downloads, available on the Collins site, includes PDF and editable Word versions of all the resources in the Teacher’s Guide, to print and adapt, as well as source files, mapping grids, worksheets, resource sheets and recommended sites to support practical tasks with a host of resources to supplement teaching. Written by Flip Computing, a team of female experts who have a breadth of computing experience across industry, education and research, and their culturally responsive approach provides teachers with an accessible and inclusive learning experience for all ages and inspires greater participation and tech career pathways for all. Supports teachers to prepare learners for a seamless transition to Lower Secondary Computing Stage 8.
Multi Faceted Deep Learning
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Author : Jenny Benois-Pineau
language : en
Publisher: Springer Nature
Release Date : 2021-10-20
Multi Faceted Deep Learning written by Jenny Benois-Pineau and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-10-20 with Computers categories.
This book covers a large set of methods in the field of Artificial Intelligence - Deep Learning applied to real-world problems. The fundamentals of the Deep Learning approach and different types of Deep Neural Networks (DNNs) are first summarized in this book, which offers a comprehensive preamble for further problem–oriented chapters. The most interesting and open problems of machine learning in the framework of Deep Learning are discussed in this book and solutions are proposed. This book illustrates how to implement the zero-shot learning with Deep Neural Network Classifiers, which require a large amount of training data. The lack of annotated training data naturally pushes the researchers to implement low supervision algorithms. Metric learning is a long-term research but in the framework of Deep Learning approaches, it gets freshness and originality. Fine-grained classification with a low inter-class variability is a difficult problem for any classification tasks. This book presents how it is solved, by using different modalities and attention mechanisms in 3D convolutional networks. Researchers focused on Machine Learning, Deep learning, Multimedia and Computer Vision will want to buy this book. Advanced level students studying computer science within these topic areas will also find this book useful.
A Survey Of Machine Learning Models For Prediabetes Screening
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Author : Amos Olwendo
language : en
Publisher: GRIN Verlag
Release Date : 2025-03-13
A Survey Of Machine Learning Models For Prediabetes Screening written by Amos Olwendo and has been published by GRIN Verlag this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-13 with Computers categories.
Scientific Study from the year 2025 in the subject Communications - Multimedia, Internet, New Technologies, grade: 18.0, Kenyatta University, language: English, abstract: Diabetes is gradually becoming a global challenge owing to the gradual increase in the number of cases of Type 2 diabetes mellitus (T2DM). T2DM is characterized as a state of hyperglycaemia due to abnormal control of insulin levels that eventually affects metabolism. This study aimed to review articles that implement machine learning methods to identify suitable risk factors for prediabetes. The study adopted the preferred reporting items for systematic review (PRISMA) protocol and research questions were formulated by the identification of synonyms and related terms "predictors and prediabetes and machine learning" from PubMed and Google scholar. Both observational and interventional original articles that were published between 2018 and 2023 were included in this study. Eligibility for inclusion was determined by scanning the article title, abstract, and study methodology section.
Machine Learning And Deep Learning Using Python And Tensorflow
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Author : Venkata Reddy Konasani
language : en
Publisher: McGraw Hill Professional
Release Date : 2021-04-29
Machine Learning And Deep Learning Using Python And Tensorflow written by Venkata Reddy Konasani and has been published by McGraw Hill Professional this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-29 with Technology & Engineering categories.
Understand the principles and practices of machine learning and deep learning This hands-on guide lays out machine learning and deep learning techniques and technologies in a style that is approachable, using just the basic math required. Written by a pair of experts in the field, Machine Learning and Deep Learning Using Python and TensorFlow contains case studies in several industries, including banking, insurance, e-commerce, retail, and healthcare. The book shows how to utilize machine learning and deep learning functions in today’s smart devices and apps. You will get download links for datasets, code, and sample projects referred to in the text. Coverage includes: Machine learning and deep learning concepts Python programming and statistics fundamentals Regression and logistic regression Decision trees Model selection and cross-validation Cluster analysis Random forests and boosting Artificial neural networks TensorFlow and Keras Deep learning hyperparameters Convolutional neural networks Recurrent neural networks and long short-term memory