Playing around with PyTorch as I review, explore, and re-explore Deep Learning basics, practices, and implementation strategies.
pytorch_fun
┣ data -----------------------> Data lives here locally
┃ ┗ .gitkeep
┣ docs
┃ ┗ resources.md -------------> Some resources I've used
┣ models
┃ ┣ early_adversarial_droput --> Early stopping, adversarial training, and dropout
┃ ┃ ┣ adversarial.py
┃ ┃ ┗ training.ipynb
┃ ┣ weight_decay --------------> L1/L2 weight decay regularization
┃ ┃ ┣ nets.py
┃ ┃ ┗ training.ipynb
┃ ┗ xor ----------------------> XOR implementation with 2D MLP
┃ ┃ ┣ net.py
┃ ┃ ┗ training.ipynb
┣ .gitignore
┣ mixin.py -------------------> Mixin nn.Module for basic boiler plate
┗ README.md