Argomenti del programma: Neural networks are mature, flexible, and powerful non-linear data-driven models that have successfully been applied to solve complex tasks in science and engineering. The advent of the deep learning paradigm, i.e., the use of (neural) network to simultaneously learn an optimal data representation and the corresponding model, has further boosted neural networks and the data-driven paradigm.
Argomenti del programma: The course topics include: Image models based on orthonormal bases (DCT), data-driven bases (PCA) Image models based on sparsity and redundancy: Away from an Orthonormal Basis, representations with respect to a redundant set of generators. Sparse coding with ℓ^0 (OMP) or ℓ^1 norm (convex optimization ISTA, IRLS, LASSO), extended models (Group LASSO) Dictionaries yielding sparse representations and dictionary…
Politecnico di Milano · Scuola di Ingegneria Industriale e dell'Informazione · 5 CFU · apri nel catalogo
Il docente non ha ancora pubblicato i testi per questo canale.
Bacheca del docente: cosa indica di studiare
Argomenti del programma: Course Introduction: a historical perspective on Deep Learning with key steps in the evolution of learning techniques, deep learning models, and deep models investigation techniques. Deep Learning in Non-Supervised Settings: Unsupervised DL models (Autoencoders), self-supervised learning practices for pre-training, metric-based and zero-shot Learning, and knowledge distillation.
Argomenti del programma: Neural networks are mature, flexible, and powerful non-linear data-driven models that have successfully been applied to solve complex tasks in science and engineering. The advent of the deep learning paradigm, i.e., the use of (neural) network to simultaneously learn an optimal data representation and the corresponding model, has further boosted neural networks and the data-driven paradigm.
Politecnico di Milano · Scuola di Ingegneria Industriale e dell'Informazione · 5 CFU · apri nel catalogo
Il docente non ha ancora pubblicato i testi per questo canale.
Bacheca del docente: cosa indica di studiare
Argomenti del programma: Course Introduction: a historical perspective on Deep Learning with key steps in the evolution of learning techniques, deep learning models, and deep models investigation techniques. Deep Learning in Non-Supervised Settings: Unsupervised DL models (Autoencoders), self-supervised learning practices for pre-training, metric-based and zero-shot Learning, and knowledge distillation.
Argomenti del programma: The course topics include: Image models based on orthonormal bases (DCT), data-driven bases (PCA) Image models based on sparsity and redundancy: Away from an Orthonormal Basis, representations with respect to a redundant set of generators. Sparse coding with ℓ^0 (OMP) or ℓ^1 norm (convex optimization ISTA, IRLS, LASSO), extended models (Group LASSO) Dictionaries yielding sparse representations and dictionary…
Argomenti del programma: The course topics include: Image models based on orthonormal bases (DCT), data-driven bases (PCA) Image models based on sparsity and redundancy: Away from an Orthonormal Basis, representations with respect to a redundant set of generators. Sparse coding with ℓ^0 (OMP) or ℓ^1 norm (convex optimization ISTA, IRLS, LASSO), extended models (Group LASSO) Dictionaries yielding sparse representations and dictionary…
Argomenti del programma: Neural networks are mature, flexible, and powerful non-linear data-driven models that have successfully been applied to solve complex tasks in science and engineering. The advent of the deep learning paradigm, i.e., the use of (neural) network to simultaneously learn an optimal data representation and the corresponding model, has further boosted neural networks and the data-driven paradigm.
Il prof ha indicato altri libri, pagine o modifiche? Scrivicelo.
Domande frequenti
Quali libri consiglia il prof. Giacomo Boracchi per Artificial Neural Networks And Deep Learning (Computer Science and Engineering, canale A-O)?
Ian Goodfellow – Deep Learning
Quali libri consiglia il prof. Giacomo Boracchi per Mathematical Models And Methods For Image Processing (Computer Science and Engineering, canale unico)?
Michael Elad – Sparse and redundant representations
Quali libri consiglia il prof. Giacomo Boracchi per Artificial Neural Networks And Deep Learning (Electronics Engineering, canale unico)?
Ian Goodfellow – Deep Learning
Quali libri consiglia il prof. Giacomo Boracchi per Mathematical Models And Methods For Image Processing (High Performance Computing Engineering, canale unico)?
Michael Elad – Sparse and redundant representations
Quali libri consiglia il prof. Giacomo Boracchi per Mathematical Models And Methods For Image Processing (Mathematical Engineering, canale unico)?
Michael Elad – Sparse and redundant representations
Quali libri consiglia il prof. Giacomo Boracchi per Deep Learning And Mathematical Models For Image Processing?
Libri Universitari