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Derived from
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20810262 Deep Learning in Computer science and engineering LM-32 GASPARETTI FABIO
(syllabus)
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 NLP
(reference books)
Hands-on machine learning with Scikit-learn Keras and TensorFlow by Aurelion Geron published by O` Reilley
Deep Learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville published by MIT Press
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Università Roma Tre