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Titlebook: Innovations in Machine and Deep Learning; Case Studies and App Gilberto Rivera,Alejandro Rosete,Nelson Rangel-Val Book 2023 The Editor(s) (

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發(fā)表于 2025-3-21 18:26:50 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Innovations in Machine and Deep Learning
副標題Case Studies and App
編輯Gilberto Rivera,Alejandro Rosete,Nelson Rangel-Val
視頻videohttp://file.papertrans.cn/468/467140/467140.mp4
概述Introduces machine and deep learning approaches for solving challenging problems.Collects the latest technological innovations and models related to deep learning.Includes representative applications
叢書名稱Studies in Big Data
圖書封面Titlebook: Innovations in Machine and Deep Learning; Case Studies and App Gilberto Rivera,Alejandro Rosete,Nelson Rangel-Val Book 2023 The Editor(s) (
描述.In recent years, significant progress has been made in achieving artificial intelligence (AI) with an impact on students, managers, scientists, health personnel, technical roles, investors, teachers, and leaders. This book presents numerous successful applications of AI in various contexts..The innovative implications covered fall under the general field of machine learning (ML), including deep learning, decision-making, forecasting, pattern recognition, information retrieval, and interpretable AI. Decision-makers and entrepreneurs will find numerous successful applications in health care, sustainability, risk management, human activity recognition, logistics, and Industry 4.0.. .This book is an essential resource for anyone interested in challenges, opportunities, and the latest developments and real-world applications of ML. Whether you are a student, researcher, practitioner, or simply curious about AI, this book provides valuable insights and inspiration for your work and learning..
出版日期Book 2023
關鍵詞Big Data Environments; Data-driven Decisions; Data Visualization; Data Warehousing; Forecasting Methods;
版次1
doihttps://doi.org/10.1007/978-3-031-40688-1
isbn_softcover978-3-031-40690-4
isbn_ebook978-3-031-40688-1Series ISSN 2197-6503 Series E-ISSN 2197-6511
issn_series 2197-6503
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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Studies in Big Datahttp://image.papertrans.cn/i/image/467140.jpg
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978-3-031-40690-4The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
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Recursive Multi-step Time-Series Forecasting for Residual-Feedback Artificial Neural Networks: A Sur analysis of 19 studies was conducted to answer these questions. Furthermore, several potential solutions that can be further practically explored are suggested in an attempt to overcome this challenge.
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