Deep Learning - Computer Vision for Beginners Using PyTorch - Writing a Custom NN Module

The video tutorial explains how to write simple models using linear operations and activations, and introduces the concept of subclassing the NN module for more complex models. It details the use of the Torch NN module to implement various layers and entire neural networks. The tutorial covers subclassing the NN module, overriding the init and forward methods, and implementing a custom class. Finally, it completes the model definition and discusses executing the code to achieve the same results as before.

What is the purpose of subclassing the NN module in PyTorch?

Describe the process of defining a model using the NN module.

What methods can be overridden when subclassing the NN module?

Explain the significance of the init function in a custom class.

What parameters are typically defined in the init method of a custom class?

How do you implement the forward function in a custom neural network class?

What modifications are necessary when using a custom class for model fitting?