Introduction to PyTorch
The session covered the basics of deep learning and PyTorch, beginning with linear regression and an explanation of how to train it using forward and backward propagation:
- Forward Propagation: The model processes input data through its functions to generate predictions.
- Backward Propagation: The neural network adjusts its parameters based on the gradient of the loss function.
Next, Multi-Layer Perceptrons (MLP) were introduced, with a discussion on how activation functions add non-linearity to neural networks.
The session then moved to PyTorch, covering basic tensor concepts, model creation, data loading, and model optimization. It concluded with a workflow example for training a model:
- Retrieve a batch from the DataLoader.
- Obtain predictions from the model for the batch.
- Calculate the loss based on the difference between predictions and labels.
- Perform backpropagation to calculate gradients for each parameter with respect to the loss.
- Update the model parameters based on the gradients.
