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Titlebook: Applied Deep Learning; Tools, Techniques, a Paul Fergus,Carl Chalmers Textbook 2022 Springer Nature Switzerland AG 2022 Deep Learning.Machi

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發(fā)表于 2025-3-21 19:57:44 | 只看該作者 |倒序瀏覽 |閱讀模式
期刊全稱Applied Deep Learning
期刊簡稱Tools, Techniques, a
影響因子2023Paul Fergus,Carl Chalmers
視頻videohttp://file.papertrans.cn/160/159774/159774.mp4
發(fā)行地址Illustrates how AI can be used to solve real-world problems within enterprise settings.Deliberately written in such a way that makes it accessible to everyone regardless of their experience.Provides a
學(xué)科分類Computational Intelligence Methods and Applications
圖書封面Titlebook: Applied Deep Learning; Tools, Techniques, a Paul Fergus,Carl Chalmers Textbook 2022 Springer Nature Switzerland AG 2022 Deep Learning.Machi
影響因子.This book focuses on the applied aspects of artificial intelligence using enterprise frameworks and technologies. The book is applied in nature and will equip the reader with the necessary skills and understanding for delivering enterprise ML technologies. It will be valuable for undergraduate and postgraduate students in subjects such as artificial intelligence and data science, and also for industrial practitioners engaged with data analytics and machine learning tasks. The book covers all of the key conceptual aspects of the field and provides a foundation for all interested parties to develop their own artificial intelligence applications..
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https://doi.org/10.1007/978-3-658-20367-2Linux (WSL2). This overcomes the limitation of accessing GPUs with NVidia Docker runtime which is covered in this chapter. Currently, the only way to access the NVidia Docker runtime and GPU in Windows is to run WSL2.
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Deploying and Hosting Machine Learning Modelsentertainment, online shopping and even our healthcare. The advanced machine learning algorithms we see today are?relatively new and consequently how we deploy these algorithms has not fully matured. Until recently this was only undertaken by large tech giants but as you will see in this chapter any
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Enterprise Machine Learning ServingLinux (WSL2). This overcomes the limitation of accessing GPUs with NVidia Docker runtime which is covered in this chapter. Currently, the only way to access the NVidia Docker runtime and GPU in Windows is to run WSL2.
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發(fā)表于 2025-3-23 03:17:52 | 只看該作者
https://doi.org/10.1007/978-3-031-04420-5Deep Learning; Machine Learning; TensorFlow; Neural Networks; Accelerated Learning; RAPIDS (Open GPU Data
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978-3-031-04422-9Springer Nature Switzerland AG 2022
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