Deep Learning - Computer Vision for Beginners Using PyTorch - Writing a Deep Neural Network

The video tutorial covers building a neural network using PyTorch. It starts with importing necessary libraries and defining model parameters, including input, hidden, and output layers. The model is constructed using NN.Sequential with linear layers and activation functions. A loss function and optimizer are defined for training. The training loop is implemented to compute predictions, calculate loss, and update model parameters. Finally, the code is executed, and results are reviewed, demonstrating how to build neural networks in PyTorch efficiently.

Describe the process of updating parameters in the optimization step.

What is the significance of the print statement added after the computation of loss?