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
teacher profile teaching materials
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.
Programme
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); Embeddings; Use cases: AlexNet, VGG, NiN, GoogLeNet/Inception, ResNet, DenseNet; Transformers, Diffusion Models, LLMs. Use cases: Computer Vision and NLP.Core Documentation
Simon J.D. Prince. "Understanding Deep Learning". MIT Press Dec 5th 2023 A. Geron,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.
Attendance
Not mandatory but strongly recommended.Type of evaluation
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. teacher profile teaching materials
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.
Mutuazione: 20810262 Deep Learning in Ingegneria informatica e dell'intelligenza artificiale LM-32 GASPARETTI FABIO
Programme
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); Embeddings; Use cases: AlexNet, VGG, NiN, GoogLeNet/Inception, ResNet, DenseNet; Transformers, Diffusion Models, LLMs. Use cases: Computer Vision and NLP.Core Documentation
Simon J.D. Prince. "Understanding Deep Learning". MIT Press Dec 5th 2023 A. Geron,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.
Attendance
Not mandatory but strongly recommended.Type of evaluation
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. teacher profile teaching materials
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.
Mutuazione: 20810262 Deep Learning in Ingegneria informatica e dell'intelligenza artificiale LM-32 GASPARETTI FABIO
Programme
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); Embeddings; Use cases: AlexNet, VGG, NiN, GoogLeNet/Inception, ResNet, DenseNet; Transformers, Diffusion Models, LLMs. Use cases: Computer Vision and NLP.Core Documentation
Simon J.D. Prince. "Understanding Deep Learning". MIT Press Dec 5th 2023 A. Geron,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.
Attendance
Not mandatory but strongly recommended.Type of evaluation
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. teacher profile teaching materials
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.
Mutuazione: 20810262 Deep Learning in Ingegneria informatica e dell'intelligenza artificiale LM-32 GASPARETTI FABIO
Programme
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); Embeddings; Use cases: AlexNet, VGG, NiN, GoogLeNet/Inception, ResNet, DenseNet; Transformers, Diffusion Models, LLMs. Use cases: Computer Vision and NLP.Core Documentation
Simon J.D. Prince. "Understanding Deep Learning". MIT Press Dec 5th 2023 A. Geron,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.
Attendance
Not mandatory but strongly recommended.Type of evaluation
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. teacher profile teaching materials
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.
Mutuazione: 20810262 Deep Learning in Ingegneria informatica e dell'intelligenza artificiale LM-32 GASPARETTI FABIO
Programme
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); Embeddings; Use cases: AlexNet, VGG, NiN, GoogLeNet/Inception, ResNet, DenseNet; Transformers, Diffusion Models, LLMs. Use cases: Computer Vision and NLP.Core Documentation
Simon J.D. Prince. "Understanding Deep Learning". MIT Press Dec 5th 2023 A. Geron,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.
Attendance
Not mandatory but strongly recommended.Type of evaluation
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.