Deep Learning

This
course will cover several techniques involved in neural networks such as perseptrons, feedforward networks, backpropagation and Stochastic Gradient Decent. We will also cover some modern advances, such as Convolutional Networks and Max-Pooling, Recurrent Networks, Long Short Term Memory, Reinforcement Learning, Boltzmand Machines and Deep Belief Networks, Generative Adversarial Networks, Autoencoders, Dropout and regularization.

Referências:
LI DENG, DONG YU – Deep Learning: Methods and Applications (2014).
GOODFELLOW, Ian. Deep learning (2016).

* Ementa básica. O professor tem autonomia para efetuar qualquer alteração.