Day 146: Building a Simple Neural Network in PyTorch

Agenda

• Translate the computational graph from Day 145 into a full nn.Module — the standard building block of every PyTorch model in production

• Wire together layers, activation functions, a loss function, and an optimizer into a working training loop

• Train a two-layer network on real tabular data and read the metrics that engineers use to confirm a model is actually learning

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Why This Matters

Yesterday you held a raw tensor and watched autograd track every gradient through the graph. That was the engine exposed. Today you bolt the body panels on: torch.nn gives you a composable API that every serious PyTorch codebase — from Meta’s recommendation systems to Waymo’s perception stack — is built on top of. Understanding nn.Module at this level is not academic; it is the exact mental model you need to read open-source model code, debug training failures, and eventually swap in your own custom layers without breaking anything downstream.