Updated PyTorch Notes - Base notes from Patrick Loeber

1. Installation
2. Tensor Basics
3. Autograd
4. Backpropagation
5. Gradient Descent With Autograd and Backpropagation
6. Training Pipeline: Model, Loss, and Optimizer
7. Linear Regression
8. Logistic Regression
9. Dataset and DataLoader
10. Dataset Transforms
11. Softmax And Cross Entropy
12. Activation Functions
13. Feed-Forward Neural Net
14. Convolutional Neural Net (CNN)
15. Transfer Learning
16. Tensorboard
17. Save and Load Models