| Titre : |
Deep learning : foundations and concepts |
| Type de document : |
texte imprimé |
| Auteurs : |
Christopher M. Bishop (1959-....), Auteur ; Hugh Bishop, Auteur |
| Editeur : |
Cham : Springer International Publishing |
| Année de publication : |
2024 |
| Importance : |
1 vol. (XX-649 p.) |
| Présentation : |
ill. en noir et en coul. |
| Format : |
25 cm |
| ISBN/ISSN/EAN : |
978-3-031-45467-7 |
| Note générale : |
PPN 276470680 |
| Langues : |
Anglais (eng) |
| Tags : |
Intelligence artificielle -- Manuels d'enseignement supérieur Apprentissage profond -- Manuels d'enseignement supérieur Artificial intelligence -- Textbooks Machine learning -- Textbooks
Artificial intelligence -- Data processing -- Textbooks |
| Index. décimale : |
006.31 Apprentissage automatique (informatique) |
| Résumé : |
"This book offers a comprehensive introduction to the central ideas that underpin deep learning. It is intended both for newcomers to machine learning and for those already experienced in the field. Covering key concepts relating to contemporary architectures and techniques, this essential book equips readers with a robust foundation for potential future specialization. The field of deep learning is undergoing rapid evolution, and therefore this book focusses on ideas that are likely to endure the test of time. The book is organized into numerous bite-sized chapters, each exploring a distinct topic, and the narrative follows a linear progression, with each chapter building upon content from its predecessors. This structure is well-suited to teaching a two-semester undergraduate or postgraduate machine learning course, while remaining equally relevant to those engaged in active research or in self-study. A full understanding of machine learning requires some mathematical background and so the book includes a self-contained introduction to probability theory. However, the focus of the book is on conveying a clear understanding of ideas, with emphasis on the real-world practical value of techniques rather than on abstract theory. Complex concepts are therefore presented from multiple complementary perspectives including textual descriptions, diagrams, mathematical formulae, and pseudo-code." (4e de couverture) |
| Note de contenu : |
Contient des exercices
Bibliographie p. 625-640. Index
Sommaire : 1. The deep learning revolution -2. Probabilities -3. Standard distributions -4. Single-layer networks: regression -5. Single-layer networks: classification -6. Deep neural networks -7. Gradient descent -8. Backpropagation -9. Regularization -10. Convolutional networks -11. Structured distributions -12. Transformers -13. Graph neural networks -14. Sampling -15. Discrete latent variables -16. Continuous latent variables -17. Generative adversarial networks -18. Normalizing flows -19. Autoencoders -20. Diffusion models - Appendix A. Linear algebra - Appendix B. Calculus of variations - Appendix C. Lagrange multipliers - |
Deep learning : foundations and concepts [texte imprimé] / Christopher M. Bishop (1959-....), Auteur ; Hugh Bishop, Auteur . - Cham : Springer International Publishing, 2024 . - 1 vol. (XX-649 p.) : ill. en noir et en coul. ; 25 cm. ISBN : 978-3-031-45467-7 PPN 276470680 Langues : Anglais ( eng)
| Tags : |
Intelligence artificielle -- Manuels d'enseignement supérieur Apprentissage profond -- Manuels d'enseignement supérieur Artificial intelligence -- Textbooks Machine learning -- Textbooks
Artificial intelligence -- Data processing -- Textbooks |
| Index. décimale : |
006.31 Apprentissage automatique (informatique) |
| Résumé : |
"This book offers a comprehensive introduction to the central ideas that underpin deep learning. It is intended both for newcomers to machine learning and for those already experienced in the field. Covering key concepts relating to contemporary architectures and techniques, this essential book equips readers with a robust foundation for potential future specialization. The field of deep learning is undergoing rapid evolution, and therefore this book focusses on ideas that are likely to endure the test of time. The book is organized into numerous bite-sized chapters, each exploring a distinct topic, and the narrative follows a linear progression, with each chapter building upon content from its predecessors. This structure is well-suited to teaching a two-semester undergraduate or postgraduate machine learning course, while remaining equally relevant to those engaged in active research or in self-study. A full understanding of machine learning requires some mathematical background and so the book includes a self-contained introduction to probability theory. However, the focus of the book is on conveying a clear understanding of ideas, with emphasis on the real-world practical value of techniques rather than on abstract theory. Complex concepts are therefore presented from multiple complementary perspectives including textual descriptions, diagrams, mathematical formulae, and pseudo-code." (4e de couverture) |
| Note de contenu : |
Contient des exercices
Bibliographie p. 625-640. Index
Sommaire : 1. The deep learning revolution -2. Probabilities -3. Standard distributions -4. Single-layer networks: regression -5. Single-layer networks: classification -6. Deep neural networks -7. Gradient descent -8. Backpropagation -9. Regularization -10. Convolutional networks -11. Structured distributions -12. Transformers -13. Graph neural networks -14. Sampling -15. Discrete latent variables -16. Continuous latent variables -17. Generative adversarial networks -18. Normalizing flows -19. Autoencoders -20. Diffusion models - Appendix A. Linear algebra - Appendix B. Calculus of variations - Appendix C. Lagrange multipliers - |
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