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About

This repository supplies code and tips for deep learning's researchers and/or pratictioners who crave to learn Pytorch. It's based on Pytorch Deep Learning begginers' tutorial, Python Engineer's Deep Learning with Pytorch free course, and my own notes. I'll update it frequently, adding features not only restricted to the field of Deep Learning, but also from the more broad AI field. Hope you enjoy!

Installation

• Go to Pytorch website
• Select appropriate version (preferable the stable one), OS, programming language (e.g., Python for this tutorial), and CUDA version or CPU only
• Set a virtual environment and install
• Test if everything is correct, trying to import torch package

Table of Contents

1. Basics
   1. Tensors
   2. Autograd
   3. Backpropagation
   4. Gradient Descent
   5. Softmax and Crossentropy
   6. Activation Functions
   7. Training Pipeline
   8. Dataset and Dataloader
   9. Dataset Transforms
2. Supervised Learning
   1. Linear Regression
   2. Logistic Regression
   3. Feedforward Neural Networks
   4. Convolutional Neural Networks
3. Unsupervised Learning
4. Reinforcement Learning
5. Transfer Learning
6. Tensorboard
7. Save and Load Models

Credits

Support

Contact: caio.netto@usp.br