Lesson 3: Building a Simple NN with PyTorch

Writing raw mathematical gradients from scratch is tedious and error-prone. Modern deep learning relies on high-level frameworks. Let's learn how to build neural networks using **PyTorch**, the primary deep learning library for researchers worldwide.

Introduction to PyTorch

PyTorch is built around two core features: **Tensors** (similar to NumPy arrays but with GPU acceleration support) and **Autograd** (automatic differentiation that tracks mathematical calculations to automatically compute gradients).

Defining Layers with torch.nn

The torch.nn module provides pre-packaged modules for standard neural network structures. A linear layer (fully connected layer) is defined as nn.Linear(in_features, out_features):

import torch
import torch.nn as nn

# Linear layer mapping 3 inputs to 2 outputs
layer = nn.Linear(3, 2)

Stacking Layers with nn.Sequential

To stack layers in a clean forward pipeline, we use nn.Sequential. It chains layers together so the output of one layer automatically becomes the input of the next.

Gradient Calculations with backward()

When you make a forward prediction and calculate the loss, running loss.backward()automatically calculates all gradients via the backpropagation chain rule. PyTorch tracks every operation, saving you from writing tedious calculus.