Data Centric Training Model
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Data Centric Training Model
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Author : Gerard Blokdyk
language : en
Publisher: Createspace Independent Publishing Platform
Release Date : 2017-10-15
Data Centric Training Model written by Gerard Blokdyk and has been published by Createspace Independent Publishing Platform this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-10-15 with categories.
This limited edition data centric training model self-assessment will make you the established data centric training model domain specialist by revealing just what you need to know to be fluent and ready for any data centric training model challenge. How do I reduce the effort in the data centric training model work to be done to get problems solved? How can I ensure that plans of action include every data centric training model task and that every data centric training model outcome is in place? How will I save time investigating strategic and tactical options and ensuring data centric training model opportunity costs are low? How can I deliver tailored data centric training model advise instantly with structured going-forward plans? There's no better guide through these mind-expanding questions than acclaimed best-selling author Gerard Blokdyk. Blokdyk ensures all data centric training model essentials are covered, from every angle: the data centric training model self-assessment shows succinctly and clearly that what needs to be clarified to organize the business/project activities and processes so that data centric training model outcomes are achieved. Contains extensive criteria grounded in past and current successful projects and activities by experienced data centric training model practitioners. Their mastery, combined with the uncommon elegance of the self-assessment, provides its superior value to you in knowing how to ensure the outcome of any efforts in data centric training model are maximized with professional results. Your purchase includes access to the $249 value data centric training model self-assessment dashboard download which gives you your dynamically prioritized projects-ready tool and shows your organization exactly what to do next. Your exclusive instant access details can be found in your book.
Data Centric Training Model A Clear And Concise Reference
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Author : Gerardus Blokdyk
language : en
Publisher: 5starcooks
Release Date : 2018-04-05
Data Centric Training Model A Clear And Concise Reference written by Gerardus Blokdyk and has been published by 5starcooks this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-04-05 with categories.
Is there a critical path to deliver data centric training model results? How do we go about Securing data centric training model? What are the top 3 things at the forefront of our data centric training model agendas for the next 3 years? Are we Assessing data centric training model and Risk? How do we keep improving data centric training model? This exclusive data centric training model self-assessment will make you the trusted data centric training model domain authority by revealing just what you need to know to be fluent and ready for any data centric training model challenge. How do I reduce the effort in the data centric training model work to be done to get problems solved? How can I ensure that plans of action include every data centric training model task and that every data centric training model outcome is in place? How will I save time investigating strategic and tactical options and ensuring data centric training model costs are low? How can I deliver tailored data centric training model advice instantly with structured going-forward plans? There's no better guide through these mind-expanding questions than acclaimed best-selling author Gerard Blokdyk. Blokdyk ensures all data centric training model essentials are covered, from every angle: the data centric training model self-assessment shows succinctly and clearly that what needs to be clarified to organize the required activities and processes so that data centric training model outcomes are achieved. Contains extensive criteria grounded in past and current successful projects and activities by experienced data centric training model practitioners. Their mastery, combined with the easy elegance of the self-assessment, provides its superior value to you in knowing how to ensure the outcome of any efforts in data centric training model are maximized with professional results. Your purchase includes access details to the data centric training model self-assessment dashboard download which gives you your dynamically prioritized projects-ready tool and shows you exactly what to do next. Your exclusive instant access details can be found in your book.
Data Centric Training Model A Clear And Concise Reference
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Author : Gerardus Blokdyk
language : en
Publisher:
Release Date :
Data Centric Training Model A Clear And Concise Reference written by Gerardus Blokdyk and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.
Data Centric Artificial Intelligence A Beginner S Guide
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Author : Parikshit N. Mahalle
language : en
Publisher: Springer Nature
Release Date : 2023-11-11
Data Centric Artificial Intelligence A Beginner S Guide written by Parikshit N. Mahalle and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-11-11 with Technology & Engineering categories.
This book discusses the best research roadmaps, strategies, and challenges in data-centric approach of artificial intelligence (AI) in various domains. It presents comparative studies of model-centric and data-centric AI. It also highlights different phases in data-centric approach and data-centric principles. The book presents prominent use cases of data-centric AI. It serves as a reference guide for researchers and practitioners in academia and industry.
Data Centric Machine Learning With Python
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Author : Jonas Christensen
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-02-29
Data Centric Machine Learning With Python written by Jonas Christensen and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-02-29 with Computers categories.
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.
Theory And Algorithms For Data Centric Machine Learning
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Author : Zachary Izzo
language : en
Publisher:
Release Date : 2023
Theory And Algorithms For Data Centric Machine Learning written by Zachary Izzo and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023 with categories.
Machine learning (ML) and AI have achieved remarkable, super-human performance in a wide variety of domains: computer vision, natural language processing, and protein folding, to name but a few. Until recently, most advancements have taken a model-centric approach, focusing primarily on improved neural network architectures (ConvNets, ResNets, transformers, etc.) and optimization procedures for training these models (batch norm, dropout, neural architecture search, etc.). Relatively less attention has been paid to the data used to train these models, in spite of the well-known fact that ML is critically dependent on high-quality data, captured succinctly with the phrase "garbage in, garbage out." As the returns on ever larger and more complicated models diminish (MT-NLG from Nvidia and Microsoft having 530B parameters), researchers have begun to realize the importance of taking a data-centric approach and developing principled methods for studying the fuel for these models: the data itself. Beyond improved task performance, a data-centric perspective also allows us to take socially critical considerations, such as data privacy, into account. In this thesis, we will take a critical look at several points in the ML data pipeline: before, during, and after model training. Before model training, we will explore the problem of data selection: which data should be used to train the model, and on what type of data should we expect our model to work? As we move forward into model training, we will turn our attention to two issues which can result from the interaction of our ML systems with the environment in which they are deployed. The first issue is that of data privacy: how can we prevent our models from leaking sensitive information about their training data? The second issue concerns the dynamic nature of some modeled populations. Especially when our model is used to make socially impactful decisions (e.g., automated loan approval or recommender systems), the model itself may impact the distribution of the data, leading to degraded performance. Lastly, despite following best practices before and during model training, it may be the case that we want to post-process a model to remove the effects of certain data after training. How can this be achieved in a computationally efficient manner? This thesis covers novel solutions for each of the preceding problems, with an emphasis on the provable guarantees for each of the proposed algorithms. By applying mathematical rigor to challenging real-world problems, we can develop algorithms which are both effective and trustworthy.
Data Centric Artificial Intelligence For Multidisciplinary Applications
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Author : Parikshit N Mahalle
language : en
Publisher: CRC Press
Release Date : 2024-06-06
Data Centric Artificial Intelligence For Multidisciplinary Applications written by Parikshit N Mahalle and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-06-06 with Computers categories.
This book explores the need for a data‐centric AI approach and its application in the multidisciplinary domain, compared to a model‐centric approach. It examines the methodologies for data‐centric approaches, the use of data‐centric approaches in different domains, the need for edge AI and how it differs from cloud‐based AI. It discusses the new category of AI technology, "data‐centric AI" (DCAI), which focuses on comprehending, utilizing, and reaching conclusions from data. By adding machine learning and big data analytics tools, data‐centric AI modifies this by enabling it to learn from data rather than depending on algorithms. It can therefore make wiser choices and deliver more precise outcomes. Additionally, it has the potential to be significantly more scalable than conventional AI methods. • Includes a collection of case studies with experimentation results to adhere to the practical approaches • Examines challenges in dataset generation, synthetic datasets, analysis, and prediction algorithms in stochastic ways • Discusses methodologies to achieve accurate results by improving the quality of data • Comprises cases in healthcare and agriculture with implementation and impact of quality data in building AI applications
Data Centric Explanations
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Author : Md Ariful Islam Anik
language : en
Publisher:
Release Date : 2020
Data Centric Explanations written by Md Ariful Islam Anik and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.
Training datasets fundamentally impact the performance of machine learning systems. Any biases introduced during training (implicit or explicit) are often reflected in the system's behaviors leading to questions about fairness and loss of trust in the system. Yet, information on training data is rarely communicated to the stakeholders. In this thesis, I explore the concept of data-centric explanations for machine learning systems that describe the training data to end-users. I design data-centric explanations that focus on providing information on training data. Through a formative study, I investigate the potential utility of such an approach and the data-centric information that users find most compelling. In a second study, I investigate reactions to the explanations across four different system scenarios. The results show that data-centric explanations can impact how users judge the trustworthiness of a system and can assist users in assessing fairness. I discuss the implications of the findings for designing explanations to support users' perception of machine learning systems.
Data Driven Computational Neuroscience
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Author : Concha Bielza
language : en
Publisher: Cambridge University Press
Release Date : 2020-11-26
Data Driven Computational Neuroscience written by Concha Bielza and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-26 with Computers categories.
Trains researchers and graduate students in state-of-the-art statistical and machine learning methods to build models with real-world data.
Data Driven Modeling Of Cyber Physical Systems Using Side Channel Analysis
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Author : Sujit Rokka Chhetri
language : en
Publisher: Springer Nature
Release Date : 2020-02-08
Data Driven Modeling Of Cyber Physical Systems Using Side Channel Analysis written by Sujit Rokka Chhetri and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-02-08 with Technology & Engineering categories.
This book provides a new perspective on modeling cyber-physical systems (CPS), using a data-driven approach. The authors cover the use of state-of-the-art machine learning and artificial intelligence algorithms for modeling various aspect of the CPS. This book provides insight on how a data-driven modeling approach can be utilized to take advantage of the relation between the cyber and the physical domain of the CPS to aid the first-principle approach in capturing the stochastic phenomena affecting the CPS. The authors provide practical use cases of the data-driven modeling approach for securing the CPS, presenting novel attack models, building and maintaining the digital twin of the physical system. The book also presents novel, data-driven algorithms to handle non- Euclidean data. In summary, this book presents a novel perspective for modeling the CPS.