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Record Nr. |
UNINA9910746070603321 |
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Autore |
Yan Wei Qi |
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Titolo |
Computational Methods for Deep Learning : Theory, Algorithms, and Implementations / / by Wei Qi Yan |
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Pubbl/distr/stampa |
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Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2023 |
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ISBN |
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Edizione |
[2nd ed. 2023.] |
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Descrizione fisica |
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1 online resource (235 pages) |
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Collana |
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Texts in Computer Science, , 1868-095X |
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Disciplina |
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Soggetti |
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Machine learning |
Neural networks (Computer science) |
Computer science - Mathematics |
Image processing - Digital techniques |
Computer vision |
Artificial intelligence |
Machine Learning |
Mathematical Models of Cognitive Processes and Neural Networks |
Mathematics of Computing |
Computer Imaging, Vision, Pattern Recognition and Graphics |
Artificial Intelligence |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Nota di contenuto |
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1. Introduction -- 2. Deep Learning Platforms -- 3. CNN and RNN -- 4. Autoencoder and GAN -- 5. Reinforcement Learning -- 6. CapsNet and Manifold Learning -- 7. Boltzmann Machines -- 8. Transfer Learning and Ensemble Learning. |
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Sommario/riassunto |
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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 |
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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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