Neural Networks and Structured Training in PyTorch

This phase checks whether the learner understands how real PyTorch models are structured, trained, saved, and reused, not just how to write tensor math.

Section A: Understanding nn.Module (Q1–Q6)

Q1. Purpose of nn.Module

Question Description:

Explain why PyTorch provides the nn.Module class and what problems it solves compared to writing raw tensor code. Focus on structure, readability, and parameter management.

Sample Input:

class MyModel(nn.Module)

Sample Output:

nn.Module provides a structured way to define, organize, and manage model parameters.

Q2. What Happens When a Class Inherits nn.Module

Question Description:

Explain what functionality a class gains when it inherits from nn.Module, especially regarding parameter tracking and training behavior.

Sample Input:

class Net(nn.Module)

Sample Output:

PyTorch automatically tracks parameters and gradients inside the model.