Deep Learning

Assignments and presentation developed in the scope of the Deep Learning discipline, taught by Professor Dário Oliveira (FGV EMAp). Co-authored with @anacarolerthal, whom I thank for the ongoing partnership.

Authors

Felipe Lamarca

Ana Carolina Erthal

Published

December 5, 2023

Assignment 1: Transfer Learning (10/10)

Assignment 2: Semantic Segmentation (10/10)

Assignment 3: Action Recognition (10/10)

Assignment 4: Generative Adversarial Networks + Autoencoders (10/10)

Assignment 5: Deep \(k\)-Means (10/10)

Presentation: YUN, S. et al. Graph Transformer Networks. In: NeurIPS, 2019. [pdf] [slides]

All notebooks were ran on Google Colab.

The repository also contains the cheatsheets folder with some useful concepts of Deep Learning I have written down during the course.

Citation

BibTeX citation:

@online{lamarca2023,
  author = {Lamarca, Felipe and Carolina Erthal, Ana},
  title = {Deep {Learning}},
  date = {2023-12-05},
  url = {https://github.com/felipelmc/Deep-Learning},
  langid = {en}
}

For attribution, please cite this work as:

Lamarca, Felipe, and Ana Carolina Erthal. 2023. “Deep Learning.” December 5, 2023. https://github.com/felipelmc/Deep-Learning.