Deep Learning
Mathematical Foundations for Neural Networks. 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.
Basic Information
Workload
60 hours
Requirements
Machine Learning
Mandatory:
- GOODFELLOW, Ian et al. Deep learning. Cambridge: MIT press, 2016. Disponível em: http://www.deeplearningbook.org/
- TALWALKAR, Ameet. Neural Networks and Deep Learning. Neural Networks, n. 1/16, 2015. Disponível em: http://neuralnetworksanddeeplearning.com/
- ANDREW NG. Machine Learning Yearning. Disponível em: 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.
- Hastie, T., Tibshirani, R., Friedman, J. (2013) The elements of statistical learning. Springer.
- Luiz André Barroso and Jimmy Clidaras and Urs Hölzle. The Datacenter as a Computer: An Introduction to the Design of Warehouse-Scale Machines. Morgan & Claypool Publishers (2013)