Basic PyTorch Pipline.

1. Design the Model (input, output size)
2. Construct the loss and optimizers
3. Training Loop

• Forward pass: compute predictions
• Backward Pass: Gradients
• Update Weights

Different ways to load dataset explained in GoogleColab Notebook Number (4).

1. Using PyTorch inbuilt datasets
2. Loading dataset from a CSV file
3. Loading dataset from a single folder with a CSV file that maps image name to the class
4. Loading dataset from multiple folders seperated according to the classes
5. Using HDF5 file for loading dataset.

GoogleColab Notebook List.

0. PyTorch Basic Syntax
1. Linear Regression using numpy and it's step by step conversion to PyTorch code
2. Linear Regression
3. Logistic Regression
4. Custom Dataset loading and creating dataset class
5. Creating First Feed Forward NN and testing it on MNIST dataset
6. Creating First CNN Model and testing it on CIFAR-10 dataset
7. Saving and Loading models