Move beyond fixed rules and discover how machines automatically learn relationships from raw data. In this lecture, we demystify the core architecture of deep learning—from individual perceptrons to deep, multi-layered networks. You will get a behind-the-scenes look at the fundamental engineering loop that makes AI "learn": measuring error with loss functions, passing blame backward using backpropagation, and taking calculated steps toward accuracy via gradient descent. By the end of this session, you’ll understand exactly how weights, biases, and optimizers transform a network from making wild guesses into a powerful predictive engine.

A practical walkthrough translating Deep Learning theory into functional PyTorch code. We will build, train, and save a custom three-layer neural network (nn.Module) from scratch that learns to compare numbers. Key topics include configuring layers (nn.Linear), using ReLU and LogSoftmax activations, generating synthetic Tensor datasets, and executing the training loop using the Adam optimizer and Negative Log Likelihood loss. Perfect for seeing how backward() and step() automate gradient descent to turn raw inputs into accurate predictions.