Machine Learning for Engineers
portes grátis
Machine Learning for Engineers
Simeone, Osvaldo
Cambridge University Press
11/2022
450
Dura
Inglês
9781316512821
15 a 20 dias
Descrição não disponível.
- Part I. Introduction and Background: 1. When and how to use machine learning
- 2. Background. Part II. Fundamental Concepts and Algorithms: 3. Inference, or model-driven prediction
- 4. Supervised learning: getting started
- 5. Optimization for machine learning
- 6. Supervised learning: beyond least squares
- 7: Unsupervised learning. Part III. Advanced Tools and Algorithms: 8. Statistical learning theory
- 9. Exponential family of distributions
- 10. Variational inference and variational expectation maximization
- 11. Information-theoretic inference and learning
- 12. Bayesian learning. Part IV. Beyond Centralized Single-Task Learning: 13. Transfer learning, multi-task learning, continual learning, and meta-learning
- 14. Federated learning. Part V. Epilogue: 15. Beyond this book.
- 2. Background. Part II. Fundamental Concepts and Algorithms: 3. Inference, or model-driven prediction
- 4. Supervised learning: getting started
- 5. Optimization for machine learning
- 6. Supervised learning: beyond least squares
- 7: Unsupervised learning. Part III. Advanced Tools and Algorithms: 8. Statistical learning theory
- 9. Exponential family of distributions
- 10. Variational inference and variational expectation maximization
- 11. Information-theoretic inference and learning
- 12. Bayesian learning. Part IV. Beyond Centralized Single-Task Learning: 13. Transfer learning, multi-task learning, continual learning, and meta-learning
- 14. Federated learning. Part V. Epilogue: 15. Beyond this book.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
- Part I. Introduction and Background: 1. When and how to use machine learning
- 2. Background. Part II. Fundamental Concepts and Algorithms: 3. Inference, or model-driven prediction
- 4. Supervised learning: getting started
- 5. Optimization for machine learning
- 6. Supervised learning: beyond least squares
- 7: Unsupervised learning. Part III. Advanced Tools and Algorithms: 8. Statistical learning theory
- 9. Exponential family of distributions
- 10. Variational inference and variational expectation maximization
- 11. Information-theoretic inference and learning
- 12. Bayesian learning. Part IV. Beyond Centralized Single-Task Learning: 13. Transfer learning, multi-task learning, continual learning, and meta-learning
- 14. Federated learning. Part V. Epilogue: 15. Beyond this book.
- 2. Background. Part II. Fundamental Concepts and Algorithms: 3. Inference, or model-driven prediction
- 4. Supervised learning: getting started
- 5. Optimization for machine learning
- 6. Supervised learning: beyond least squares
- 7: Unsupervised learning. Part III. Advanced Tools and Algorithms: 8. Statistical learning theory
- 9. Exponential family of distributions
- 10. Variational inference and variational expectation maximization
- 11. Information-theoretic inference and learning
- 12. Bayesian learning. Part IV. Beyond Centralized Single-Task Learning: 13. Transfer learning, multi-task learning, continual learning, and meta-learning
- 14. Federated learning. Part V. Epilogue: 15. Beyond this book.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
