COC 891 - Deep Learning

Professor(es)


Alexandre Gonçalves Evsukoff

Ementa

i. Introdução
ii. Plataformas: Café, TensorFlow, Theano, CNTK, Torch
iii. Autoencoders e Stacked Autoencoders
iv. Deep Boltzman Machines
v. Deep Belief Networks
vi. Convolutional Neural Networks
vii. Redes neurais recorrentes.
viii. Generative Adversarial Models
ix. Deep Reinforcement Learning
x. Aplicações em processamento de linguagem natural
xi. Aplicações em visão computacional

Bibliografia

[1] Deep Learning, Yoshua Bengio, Ian Goodfellow, Aaron Courville, MIT Press
[2] Representation Learning: A Review and New Perspectives, Yoshua Bengio, Aaron Courville, Pascal Vincent, Arxiv, 2012.
[3] Deep Machine Learning – A New Frontier in Artificial Intelligence Research – a survey paper by Itamar Arel, Derek C. Rose, and Thomas P. Karnowski.
[4] Graves, A. (2012). Supervised sequence labelling with recurrent neural networks(Vol. 385). Springer.
[5] Schmidhuber, J. (2014). Deep Learning in Neural Networks: An Overview.
[6] LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. “Deep learning.” Nature 521, no. 7553 (2015): 436-444.

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