Neural Networks and Deep Learning

Basic information

Workload: 

45 hours

Prerequisite: 

Foundations of Machine Learning
 

Syllabus: 

Perceptrons and Multi-Layer Perceptrons. Deep Learn. Feedforward networks. Backpropagation. Regularization. Performance. Learning Assessment. Neural Network Tasks and Architectures: Convolutional Neural Networks (CNNs). Sequential Models: Recurrent Neural Networks (RNNs). Long Short Term Memory Networks (LSTMs). Generative Adversarial Networks (GANs). Transfer Learning. Hopfield Networks. Boltzmann Machine Network. Deep Belief Networks. Deep Auto-encoders. Capsule Networks. Deep Learning for PLN. Deep Learn research. Open Source models. Algorithms. Hardware and Software Platforms. Examples.

 

Teaching Plan

Bibliography

Mandatory: 

·       GOODFELLOW, Ian et al. Deep learning. Cambridge: MIT press, 2016. .Available at: : http://www.deeplearningbook.org/
·       TALWALKAR, Ameet. Neural Networks and Deep Learning. Neural Networks, n. 1/16, 2015. Available at: : http://neuralnetworksanddeeplearning.com/
·       ANDREW NG. Machine Learning Yearning. Available at: http://www.mlyearning.org/

Complementary: 

·       MCCLURE, Nick. TensorFlow machine learning cookbook. 2017.
·       PATTERSON, Josh; GIBSON, Adam. Deep Learning: A Practitioner's Approach. O'Reilly Media, Inc., 2017.
·       MICHALSKI, Ryszard S.; CARBONELL, Jaime G.; MITCHELL, Tom M. (Ed.). Machine learning: An artificial intelligence approach. Springer Science & Business Media, 2013.
·       Abu-Moustafa, Y.S., Magdon-Ismail, M., e Lin H-S. (2012) Learning from data. AMLBook.com.