Programa de Verão do LNCC – edição 2019
Jornada de Ciência de Dados (18-22/fev)

Lectures

Lecture 1  (18th of February)

• Deep Learning Overview
• ANNs basics

Lecture 2 (19th of February)

• model regularization
• on activation functions

Lecture 3 (20th of February)

• convnets
• model selection and evaluation

Lecture 4 (21th of February)

• recurrent nets
• other architectures (GANs, deep bayesian nets)
• final remarks
• tutorial PyTorch
• GD detailed

Code

• Early Stopping Example
• PyTorch Examples
• CEFET’s MLRG github code repositories

Additional Resources

• Neural Networks and Deep Learning (Michael Nielsen’s book)
• Deep Learning Book  (Goodfellow et al’s book)
• Introdução à Aprendizagem Profunda
• Machine Learning Yearning (Andrew Ng’s book)
• Deep Learning Specialization (Coursera Course)
• CS231n: Convolutional Neural Networks for Visual Recognition
• The Unreasonable Effectiveness of Recurrent Neural Networks
• Understanding LSTM Networks
• Uncertainty in Deep Learning (Yarin Gal’s PhD thesis)
• Theories of Deep Learning (Course at Stanford University)
• Deep Learning Is Not Good Enough, We Need Bayesian Deep Learning for Safe AI
• Generative Adversarial Networks – Hot Topic in Machine Learning
• gans-awesome-applications
• Some cool applications of GANs