Xiaojing Ye – författare
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6 produkter
Machine Learning for Tomographic Imaging
Inbunden, 2019
1274 kr
Lägg i varukorg
The area of machine learning, especially deep learning, has exploded in recent years, producing advances in everything from speech recognition and gaming to drug discovery. Tomographic imaging is another major area that is being transformed by machine learning, and its potential to revolutionise …
Mathematical Foundations of Deep Learning
Theory and Algorithms
Av Xiaojing Ye
Inbunden, 2026
2516 kr
Lägg i varukorg
Mathematical Foundations of Deep Learning offers a comprehensive and rigorous treatment of the mathematical principles underlying modern deep learning. The book spans core theoretical topics, from the approximation capabilities of deep neural networks and the theory and algorithms of optimal …
Mathematical Foundations of Deep Learning
Theory and Algorithms
Av Xiaojing Ye
Häftad, 2026
1043 kr
Lägg i varukorg
Mathematical Foundations of Deep Learning offers a comprehensive and rigorous treatment of the mathematical principles underlying modern deep learning. The book spans core theoretical topics, from the approximation capabilities of deep neural networks and the theory and algorithms of optimal …
Mathematical Foundations of Deep Learning
Theory and Algorithms
Av Xiaojing Ye
E-bok, 2026
1230 kr
Lägg i varukorg
Mathematical Foundations of Deep Learning offers a comprehensive and rigorous treatment of the mathematical principles underlying modern deep learning. The book spans core theoretical topics, from the approximation capabilities of deep neural networks and the theory and algorithms of optimal …
Mathematical Foundations of Deep Learning
Theory and Algorithms
Av Xiaojing Ye
E-bok, 2026
1191 kr
Lägg i varukorg
Mathematical Foundations of Deep Learning offers a comprehensive and rigorous treatment of the mathematical principles underlying modern deep learning. The book spans core theoretical topics, from the approximation capabilities of deep neural networks and the theory and algorithms of optimal …