Computational Methods for Deep Learning
indgår i Texts in Computer Science serien
- Indbinding:
- Hardback
- Sideantal:
- 244
- Udgivet:
- 16. september 2023
- Udgave:
- 23002
- Størrelse:
- 160x19x241 mm.
- Vægt:
- 535 g.
- 8-11 hverdage.
- 20. november 2024
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- 1 valgfrit digitalt ugeblad
- 20 timers lytning og læsning
- Adgang til 70.000+ titler
- Ingen binding
Abonnementet koster 75 kr./md.
Ingen binding og kan opsiges når som helst.
- 1 valgfrit digitalt ugeblad
- 20 timers lytning og læsning
- Adgang til 70.000+ titler
- Ingen binding
Abonnementet koster 75 kr./md.
Ingen binding og kan opsiges når som helst.
Beskrivelse af Computational Methods for Deep Learning
The first edition of this textbook was published in 2021. Over the past two years, we have invested in enhancing all aspects of deep learning methods to ensure the book is comprehensive and impeccable. Taking into account feedback from our readers and audience, the author has diligently updated this book.
The second edition of this textbook presents control theory, transformer models, and graph neural networks (GNN) in deep learning. We have incorporated the latest algorithmic advances and large-scale deep learning models, such as GPTs, to align with the current research trends. Through the second edition, this book showcases how computational methods in deep learning serve as a dynamic driving force in this era of artificial intelligence (AI).
This book is intended for research students, engineers, as well as computer scientists with interest in computational methods in deep learning. Furthermore, it is also well-suited for researchers exploring topics such as machine intelligence, robotic control, and related areas.
The second edition of this textbook presents control theory, transformer models, and graph neural networks (GNN) in deep learning. We have incorporated the latest algorithmic advances and large-scale deep learning models, such as GPTs, to align with the current research trends. Through the second edition, this book showcases how computational methods in deep learning serve as a dynamic driving force in this era of artificial intelligence (AI).
This book is intended for research students, engineers, as well as computer scientists with interest in computational methods in deep learning. Furthermore, it is also well-suited for researchers exploring topics such as machine intelligence, robotic control, and related areas.
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Bogen Computational Methods for Deep Learning findes i følgende kategorier:
- Business og læring > Computer og IT
- Matematik og naturvidenskab > Matematik > Anvendt matematik > Matematisk modellering
- Databehandling og informationsteknologi > Informatik > Matematisk datateori > Matematik til informatikfag
- Databehandling og informationsteknologi > Informatik > Kunstig intelligens > Machine learning
- Databehandling og informationsteknologi > Informatik > Billedbehandling
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