The course aims to provide advanced and specific skills in the area of the latest Deep neural network architectures. Particular attention will be given to multimodal models, and networks capable of analyzing complex data structures, such as graphs and multivariate time series; and deep reinforcement learning. At the end of the course, the student will be able to: adequately design and optimize Deep neural networks, be able to distinguish and evaluate different solutions, and be able to select and customize the most effective network architectures to be used in real application domains, supervised, unsupervised, or following a reinforcement learning approach. The course consists of a theoretical and methodological part on advanced and innovative concepts, and a laboratory activity in which these concepts are applied in problem solving using the latest development frameworks.
Curriculum
scheda docente
materiale didattico
“Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems”, O'Reilly Media, Inc, USA, 2019.
Programma
Introduction to DL; Training of Deep Architecture: hyperparameter tuning, batch normalization, faster optimizers, regularization ; Convolutional Neural Networks (CNN/ConvNets); Recurrent Neural Networks (GRU, LSTM, Bidirectional); Encoder-Decoder, Autoencoders, Variational Autoencoders; Attention layers ; Generative Adversarial Networks (GAN); Deep Reinforcement Learning; Embeddings; Use cases: AlexNet, VGG, NiN, GoogLeNet/Inception, ResNet, DenseNet; Use cases: Computer Vision and NLPTesti Adottati
Simon J.D. Prince. "Understanding Deep Learning". MIT Press Dec 5th 2023 A. Geron,“Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems”, O'Reilly Media, Inc, USA, 2019.
Modalità Frequenza
Not mandatory but strongly recommended.Modalità Valutazione
The assessment is based on tests taken during the course and a final exam. The ongoing tests allow the student to take a shorter final exam. All the tests are written examinations, and consist of open-ended and closed-ended questions.
scheda docente
materiale didattico
“Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems”, O'Reilly Media, Inc, USA, 2019.
Programma
Introduction to DL; Training of Deep Architecture: hyperparameter tuning, batch normalization, faster optimizers, regularization ; Convolutional Neural Networks (CNN/ConvNets); Recurrent Neural Networks (GRU, LSTM, Bidirectional); Encoder-Decoder, Autoencoders, Variational Autoencoders; Attention layers ; Generative Adversarial Networks (GAN); Deep Reinforcement Learning; Embeddings; Use cases: AlexNet, VGG, NiN, GoogLeNet/Inception, ResNet, DenseNet; Use cases: Computer Vision and NLPTesti Adottati
Simon J.D. Prince. "Understanding Deep Learning". MIT Press Dec 5th 2023 A. Geron,“Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems”, O'Reilly Media, Inc, USA, 2019.
Modalità Frequenza
Not mandatory but strongly recommended.Modalità Valutazione
The assessment is based on tests taken during the course and a final exam. The ongoing tests allow the student to take a shorter final exam. All the tests are written examinations, and consist of open-ended and closed-ended questions.
scheda docente
materiale didattico
“Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems”, O'Reilly Media, Inc, USA, 2019.
Programma
Introduction to DL; Training of Deep Architecture: hyperparameter tuning, batch normalization, faster optimizers, regularization ; Convolutional Neural Networks (CNN/ConvNets); Recurrent Neural Networks (GRU, LSTM, Bidirectional); Encoder-Decoder, Autoencoders, Variational Autoencoders; Attention layers ; Generative Adversarial Networks (GAN); Deep Reinforcement Learning; Embeddings; Use cases: AlexNet, VGG, NiN, GoogLeNet/Inception, ResNet, DenseNet; Use cases: Computer Vision and NLPTesti Adottati
Simon J.D. Prince. "Understanding Deep Learning". MIT Press Dec 5th 2023 A. Geron,“Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems”, O'Reilly Media, Inc, USA, 2019.
Modalità Frequenza
Not mandatory but strongly recommended.Modalità Valutazione
The assessment is based on tests taken during the course and a final exam. The ongoing tests allow the student to take a shorter final exam. All the tests are written examinations, and consist of open-ended and closed-ended questions.