Provide advanced and specific skills in Deep neural networks. The course consists of a theoretical part on the fundamental concepts, and laboratory activities in which these concepts are applied and developed through a software framework. At the end of the course the student will be able to: adequately train and optimize Deep neural networks; distinguish between different solutions and be able to choose and customize the most effective architectures in real-world scenarios, supervised, unsupervised or following a reinforcement learning approach.
Curriculum
teacher profile teaching materials
Deep Learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville published by MIT Press
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); Deep Reinforcement Learning; Embeddings; Use cases: AlexNet, VGG, NiN, GoogLeNet/Inception, ResNet, DenseNet; Use cases: Computer Vision and NLPCore Documentation
Hands-on machine learning with Scikit-learn Keras and TensorFlow by Aurelion Geron published by O` ReilleyDeep Learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville published by MIT Press
Type of delivery of the course
They consist of classroom lessons, exercises on MOOC platform, exercises in classroom, exercises on computers.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
Deep Learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville published by MIT Press
Mutuazione: 20810262 Deep Learning in Ingegneria informatica 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); Deep Reinforcement Learning; Embeddings; Use cases: AlexNet, VGG, NiN, GoogLeNet/Inception, ResNet, DenseNet; Use cases: Computer Vision and NLPCore Documentation
Hands-on machine learning with Scikit-learn Keras and TensorFlow by Aurelion Geron published by O` ReilleyDeep Learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville published by MIT Press
Type of delivery of the course
They consist of classroom lessons, exercises on MOOC platform, exercises in classroom, exercises on computers.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
Deep Learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville published by MIT Press
Mutuazione: 20810262 Deep Learning in Ingegneria informatica 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); Deep Reinforcement Learning; Embeddings; Use cases: AlexNet, VGG, NiN, GoogLeNet/Inception, ResNet, DenseNet; Use cases: Computer Vision and NLPCore Documentation
Hands-on machine learning with Scikit-learn Keras and TensorFlow by Aurelion Geron published by O` ReilleyDeep Learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville published by MIT Press
Type of delivery of the course
They consist of classroom lessons, exercises on MOOC platform, exercises in classroom, exercises on computers.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.