Reliable Machine Learning
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Reliable Machine Learning
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Author : Cathy Chen
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2021-10-12
Reliable Machine Learning written by Cathy Chen 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 2021-10-12 with Computers categories.
Whether you're part of a small startup or a multinational corporation, this practical book shows data scientists, software and site reliability engineers, product managers, and business owners how to run and establish ML reliably, effectively, and accountably within your organization. You'll gain insight into everything from how to do model monitoring in production to how to run a well-tuned model development team in a product organization. By applying an SRE mindset to machine learning, authors and engineering professionals Cathy Chen, Kranti Parisa, Niall Richard Murphy, D. Sculley, Todd Underwood, and featured guest authors show you how to run an efficient and reliable ML system. Whether you want to increase revenue, optimize decision making, solve problems, or understand and influence customer behavior, you'll learn how to perform day-to-day ML tasks while keeping the bigger picture in mind. You'll examine: What ML is: how it functions and what it relies on Conceptual frameworks for understanding how ML "loops" work How effective productionization can make your ML systems easily monitorable, deployable, and operable Why ML systems make production troubleshooting more difficult, and how to compensate accordingly How ML, product, and production teams can communicate effectively
Conformal Prediction For Reliable Machine Learning
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Author : Vineeth Balasubramanian
language : en
Publisher: Newnes
Release Date : 2014-04-23
Conformal Prediction For Reliable Machine Learning written by Vineeth Balasubramanian and has been published by Newnes this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-04-23 with Computers categories.
The conformal predictions framework is a recent development in machine learning that can associate a reliable measure of confidence with a prediction in any real-world pattern recognition application, including risk-sensitive applications such as medical diagnosis, face recognition, and financial risk prediction. Conformal Predictions for Reliable Machine Learning: Theory, Adaptations and Applications captures the basic theory of the framework, demonstrates how to apply it to real-world problems, and presents several adaptations, including active learning, change detection, and anomaly detection. As practitioners and researchers around the world apply and adapt the framework, this edited volume brings together these bodies of work, providing a springboard for further research as well as a handbook for application in real-world problems. - Understand the theoretical foundations of this important framework that can provide a reliable measure of confidence with predictions in machine learning - Be able to apply this framework to real-world problems in different machine learning settings, including classification, regression, and clustering - Learn effective ways of adapting the framework to newer problem settings, such as active learning, model selection, or change detection
Engineering Dependable And Secure Machine Learning Systems
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Author : Onn Shehory
language : en
Publisher: Springer Nature
Release Date : 2020-11-07
Engineering Dependable And Secure Machine Learning Systems written by Onn Shehory and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-07 with Computers categories.
This book constitutes the revised selected papers of the Third International Workshop on Engineering Dependable and Secure Machine Learning Systems, EDSMLS 2020, held in New York City, NY, USA, in February 2020. The 7 full papers and 3 short papers were thoroughly reviewed and selected from 16 submissions. The volume presents original research on dependability and quality assurance of ML software systems, adversarial attacks on ML software systems, adversarial ML and software engineering, etc.
Artificial Intelligence For Safety And Reliability Engineering
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Author : Kim Phuc Tran
language : en
Publisher: Springer Nature
Release Date : 2024-09-28
Artificial Intelligence For Safety And Reliability Engineering written by Kim Phuc Tran and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-09-28 with Technology & Engineering categories.
This book is a comprehensive exploration of the latest theoretical research, technological advancements, and real-world applications of artificial intelligence (AI) for safety and reliability engineering. Smart manufacturing relies on predictive maintenance (PdM) to ensure sustainable production systems, and the integration of AI has become increasingly prevalent in this field. This book serves as a valuable resource for researchers, practitioners, and decision-makers in manufacturing. By combining theoretical research, practical applications, and case studies, it equips readers with the necessary knowledge and tools to implement AI for safety and reliability engineering effectively in smart manufacturing contexts.
Computational Analysis And Deep Learning For Medical Care
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Author : Amit Kumar Tyagi
language : en
Publisher: John Wiley & Sons
Release Date : 2021-08-10
Computational Analysis And Deep Learning For Medical Care written by Amit Kumar Tyagi 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 2021-08-10 with Computers categories.
The book details deep learning models like ANN, RNN, LSTM, in many industrial sectors such as transportation, healthcare, military, agriculture, with valid and effective results, which will help researchers find solutions to their deep learning research problems. We have entered the era of smart world devices, where robots or machines are being used in most applications to solve real-world problems. These smart machines/devices reduce the burden on doctors, which in turn make their lives easier and the lives of their patients better, thereby increasing patient longevity, which is the ultimate goal of computer vision. Therefore, the goal in writing this book is to attempt to provide complete information on reliable deep learning models required for e-healthcare applications. Ways in which deep learning can enhance healthcare images or text data for making useful decisions are discussed. Also presented are reliable deep learning models, such as neural networks, convolutional neural networks, backpropagation, and recurrent neural networks, which are increasingly being used in medical image processing, including for colorization of black and white X-ray images, automatic machine translation images, object classification in photographs/images (CT scans), character or useful generation (ECG), image caption generation, etc. Hence, reliable deep learning methods for the perception or production of better results are a necessity for highly effective e-healthcare applications. Currently, the most difficult data-related problem that needs to be solved concerns the rapid increase of data occurring each day via billions of smart devices. To address the growing amount of data in healthcare applications, challenges such as not having standard tools, efficient algorithms, and a sufficient number of skilled data scientists need to be overcome. Hence, there is growing interest in investigating deep learning models and their use in e-healthcare applications. Audience Researchers in artificial intelligence, big data, computer science, and electronic engineering, as well as industry engineers in transportation, healthcare, biomedicine, military, agriculture.
Reliable Knowledge Discovery
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Author : Honghua Dai
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-02-23
Reliable Knowledge Discovery written by Honghua Dai 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 2012-02-23 with Computers categories.
Reliable Knowledge Discovery focuses on theory, methods, and techniques for RKDD, a new sub-field of KDD. It studies the theory and methods to assure the reliability and trustworthiness of discovered knowledge and to maintain the stability and consistency of knowledge discovery processes. RKDD has a broad spectrum of applications, especially in critical domains like medicine, finance, and military. Reliable Knowledge Discovery also presents methods and techniques for designing robust knowledge-discovery processes. Approaches to assessing the reliability of the discovered knowledge are introduced. Particular attention is paid to methods for reliable feature selection, reliable graph discovery, reliable classification, and stream mining. Estimating the data trustworthiness is covered in this volume as well. Case studies are provided in many chapters. Reliable Knowledge Discovery is designed for researchers and advanced-level students focused on computer science and electrical engineering as a secondary text or reference. Professionals working in this related field and KDD application developers will also find this book useful.
Computer Safety Reliability And Security Safecomp 2024 Workshops
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Author : Andrea Ceccarelli
language : en
Publisher: Springer Nature
Release Date : 2024-09-08
Computer Safety Reliability And Security Safecomp 2024 Workshops written by Andrea Ceccarelli and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-09-08 with Computers categories.
This book constitutes the proceedings of the Workshops held in conjunction with the 43rd International Conference on Computer Safety, Reliability, and Security, SAFECOMP 2024, which took place in Florence, Italy, during September 2024. The 36 papers included in this book were carefully reviewed and selected from a total of 64 submissions to the following workshops: DECSoS 2024 – 19th Workshop on Dependable Smart Embedded and Cyber-Physical Systems and Systems-of-Systems SASSUR 2024 - 11th International Workshop on Next Generation of System Assurance Approaches for Critical Systems TOASTS 2024 – Towards A Safer Systems’ Architecture Through Security WAISE 2024 – 7th International Workshop on Artificial Intelligence Safety Engineering
Proceedings Of The 11th International Symposium On Heating Ventilation And Air Conditioning Ishvac 2019
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Author : Zhaojun Wang
language : en
Publisher: Springer Nature
Release Date : 2020-03-19
Proceedings Of The 11th International Symposium On Heating Ventilation And Air Conditioning Ishvac 2019 written by Zhaojun Wang and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-03-19 with Technology & Engineering categories.
This book presents selected papers from the 11th International Symposium on Heating, Ventilation and Air Conditioning (ISHVAC 2019), with a focus on HVAC techniques for improving indoor environment quality and the energy efficiency of heating and cooling systems. Presenting inspiration for implementing more efficient and safer HVAC systems, the book is a valuable resource for academic researchers, engineers in industry, and government regulators.
Algorithms In Advanced Artificial Intelligence
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Author : R. N. V. Jagan Mohan
language : en
Publisher: CRC Press
Release Date : 2024-07-08
Algorithms In Advanced Artificial Intelligence written by R. N. V. Jagan Mohan and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-08 with Computers categories.
The most common form of severe dementia, Alzheimer’s disease (AD), is a cumulative neurological disorder because of the degradation and death of nerve cells in the brain tissue, intelligence steadily declines and most of its activities are compromised in AD. Before diving into the level of AD diagnosis, it is essential to highlight the fundamental differences between conventional machine learning (ML) and deep learning (DL). This work covers a number of photo-preprocessing approaches that aid in learning because image processing is essential for the diagnosis of AD. The most crucial kind of neural network for computer vision used in medical image processing is called a Convolutional Neural Network (CNN). The proposed study will consider facial characteristics, including expressions and eye movements using the diffusion model, as part of CNN’s meticulous approach to Alzheimer’s diagnosis. Convolutional neural networks were used in an effort to sense Alzheimer’s disease in its early stages using a big collection of pictures of facial expressions.
Blockchain Technologies For Sustainable Development In Smart Cities
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Author : Swarnalatha, P.
language : en
Publisher: IGI Global
Release Date : 2022-02-18
Blockchain Technologies For Sustainable Development In Smart Cities written by Swarnalatha, P. and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-02-18 with Political Science categories.
Blockchain technology has great potential to radically change our socio-economic systems by guaranteeing secure transactions between untrusted entities, reducing costs, and simplifying many processes. However, employing blockchain techniques in sustainable applications development for smart cities still has some technical challenges and limitations. Blockchain Technologies for Sustainable Development in Smart Cities investigates blockchain-enabled technology for smart city developments and big data applications. This book provides relevant theoretical frameworks and the latest empirical research findings in the area. Covering topics such as digital finance, smart city technology, and data processing architecture, this book is an essential reference for electricians, policymakers, local governments, city committees, computer scientists, IT professionals, professors and students of higher education, researchers, and academicians.