General resources
Aqui van las cosas que faltan por hacer [ ] prueba [] no hecho [X] HECHO
INTERPRETABILIDAD Machine Learning Interpretability Repo [ ] CNN maps interpretability [ ] Cnn interp colab Libro Limitations of Interpretable ML Methods
GUARDAR MODELOS Tensorboard y Tensorboard dev Weights and Biases
Notebooks de modelos implementados
Python cool feratures Machine Learning systems design
Videos data analisis jake vanderplas https://jakevdp.github.io/blog/2017/03/03/reproducible-data-analysis-in-jupyter/
jupytext guardar ipynb como md-r-py y sincronizarlos https://github.com/mwouts/jupytext https://jupytext.readthedocs.io/en/latest/using-server.html nbdev -> develop libraries using jupyter notebook
dtale para visualizar pandas en web con flask y python https://github.com/man-group/dtale
avanzado: python musings by radek osmulski https://github.com/radekosmulski/python_musings
rsna pneumonia 1st position https://github.com/i-pan/kaggle-rsna18
leer ml https://towardsdatascience.com/road-detection-using-segmentation-models-and-albumentations-libraries-on-keras-d5434eaf73a8
https://towardsdatascience.com/python-tricks-101-what-every-new-programmer-should-know-c512a9787022 https://data.mendeley.com/datasets/rscbjbr9sj/3 https://towardsdatascience.com/learn-advanced-features-for-pythons-main-data-analysis-library-in-20-minutes-d0eedd90d086
https://chatbotslife.com/a-coyote-that-ran-over-the-edge-differing-opinions-about-the-impact-of-ai-on-radiology-4b5e3860722e
https://twitter.com/francisdeng/status/931127254737932288
radai.club
https://lukeoakdenrayner.wordpress.com/education/
https://webcast.ranzcr.com/Mediasite/Play/866b9a20f10c460fa51cb2d5a8e3067c1d?catalog=163ad363-4e87-4e04-b989-e4e6c1001d88&catalog=163ad363-4e87-4e04-b989-e4e6c1001d88
https://enmilocalfunciona.io/deep-learning-basico-con-keras-parte-5-densenet/
https://stroemer.cc/resample-imbalanced-data/
https://towardsdatascience.com/smarter-ways-to-encode-categorical-data-for-machine-learning-part-1-of-3-6dca2f71b159
https://towardsdatascience.com/python-tricks-101-what-every-new-programmer-should-know-c512a9787022 https://data.mendeley.com/datasets/rscbjbr9sj/3 https://towardsdatascience.com/learn-advanced-features-for-pythons-main-data-analysis-library-in-20-minutes-d0eedd90d086
leer ml 2 pandas 1 https://towardsdatascience.com/minimal-pandas-subset-for-data-scientists-6355059629ae pandas 2 https://medium.com/analytics-and-data/become-a-pro-at-pandas-pythons-data-manipulation-library-264351b586b1 pandas 3https://towardsdatascience.com/why-and-how-to-use-pandas-with-large-data-9594dda2ea4c
tesis pneumonia with pretrained challenge https://www.kaggle.com/kmader/pneumonia-with-pretrained-template
ninite de data science packages