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

60 hours
Machine Learning


  • GOODFELLOW, Ian et al. Deep learning. Cambridge: MIT press, 2016. Disponível em:
  • TALWALKAR, Ameet. Neural Networks and Deep Learning. Neural Networks, n. 1/16, 2015. Disponível em:
  • ANDREW NG. Machine Learning Yearning. Disponível em:


  • 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.
  • 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)
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