A coherent introduction to core concepts and deep learning techniques that are critical to academic research and real-world applications.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Hui Jiang is Professor of Electrical Engineering and Computer Science at York University, where he has been since 2002. His main research interests include machine learning, particularly deep learning, and its applications to speech and audio processing, natural language processing, and computer vision. Over the past 30 years, he has worked on a wide range of research problems from these areas and published hundreds of technical articles and papers in the mainstream journals and top-tier conferences. His works have won the prestigious IEEE Best Paper Award and the ACL Outstanding Paper honor.
Inhaltsangabe
1. Introduction 2. Mathematical Foundation 3. Supervised Machine Learning (in a nutshell) 4. Feature Extraction 5. Statistical Learning Theory 6. Linear Models 7. Learning Discriminative Models in General 8. Neural Networks 9. Ensemble Learning 10. Overview of Generative Models 11. Unimodal Models 12. Mixture Models 13. Entangled Models 14. Bayesian Learning 15. Graphical Models.
