Practicing Deep Learning in Python applied in the real world.
Deep learning is a subset of machine learning in artificial intelligence (AI) that has networks capable of learning unsupervised from data that is unstructured or unlabeled. Also known as deep neural learning or deep neural network
This website intends to present the work analysis for the "Análise Econômica e Geração de Valor" class assignment.
Alternatively, run a binder container:
- Bernardo Aflalo
| Profile | Name | |
|---|---|---|
 |
Daniel Campos | (daniel.ferraz.campos@gmail.com) |
 |
Leandro Daniel | (contato@leandrodaniel.com) |
 |
Ricardo Reis | (ricardo.l.b.reis@gmail.com) |
 |
Rodrigo Goncalves | (rodrigo.goncalves@me.com) |
 |
Ygor Lima | (ygor_redesocial@hotmail.com) |
├── LICENSE
├── Makefile <- Makefile with commands like `make data` or `make train`
├── README.md <- The top-level README for developers using this project.
├── data
│ └── raw <- The original, immutable data dump.
│
├── mini-paper <- The final individual assignment given by Bernardo Aflalo
│
├── models <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks <- Jupyter notebooks.
│ └── external_examples <- Other interesting notebooks
│ │
│ └── fgv_classes <- All notebooks given by Professor Mirapalheta and Professor Hithoshi
│ │
│ └── fgv_group <- The final class assignment given by Bernardo Aflalo
│ in theirs respective classes
│
├── requirements.txt <- The requirements file for reproducing the analysis environment, e.g.
│ generated with `pip freeze > requirements.txt`
│
├── setup.py <- makes project pip installable (pip install -e .) so src can be imported
├── src <- Source code for use in this project.
│
└── tox.ini <- tox file with settings for running tox; see tox.testrun.org
Project based on the cookiecutter data science project template. #cookiecutterdatascience